GLM-4.5 Model Support (#8224)

Co-authored-by: Lifu Huang <lifu.hlf@gmail.com>
Co-authored-by: Binyao Jiang <byjiang1996@gmail.com>
Co-authored-by: Stefan He <hebiaobuaa@gmail.com>

benchmark/kernels/fused_moe_triton/benchmark_sglang_fused_moe_triton.py

@@ -33,7 +33,11 @@ def get_model_config(model_name: str, tp_size: int):
         topk = config.num_experts_per_tok
         intermediate_size = config.moe_intermediate_size
         shard_intermediate_size = 2 * intermediate_size // tp_size
-    elif config.architectures[0] in ["DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM"]:
+    elif config.architectures[0] in [
+        "DeepseekV2ForCausalLM",
+        "DeepseekV3ForCausalLM",
+        "Glm4MoeForCausalLM",
+    ]:
         E = (
             config.n_routed_experts + 1
             if config.architectures[0] in ["DeepseekV3ForCausalLM"]

benchmark/kernels/fused_moe_triton/benchmark_vllm_vs_sglang_fused_moe_triton.py

@@ -42,7 +42,11 @@ def get_model_config(model_name: str, tp_size: int):
         topk = config.num_experts_per_tok
         intermediate_size = config.moe_intermediate_size
         shard_intermediate_size = 2 * intermediate_size // tp_size
-    elif config.architectures[0] in ["DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM"]:
+    elif config.architectures[0] in [
+        "DeepseekV2ForCausalLM",
+        "DeepseekV3ForCausalLM",
+        "Glm4MoeForCausalLM",
+    ]:
         E = (
             config.n_routed_experts + 1
             if config.architectures[0] in ["DeepseekV3ForCausalLM"]

python/sglang/srt/configs/model_config.py

@@ -127,6 +127,9 @@ class ModelConfig:
         ):
             self.hf_config.architectures[0] = "DeepseekV3ForCausalLMNextN"
 
+        if is_draft_model and self.hf_config.architectures[0] == "Glm4MoeForCausalLM":
+            self.hf_config.architectures[0] = "Glm4MoeForCausalLMNextN"
+
         if is_draft_model and self.hf_config.architectures[0] == "MiMoForCausalLM":
             self.hf_config.architectures[0] = "MiMoMTP"
         # Check model type

python/sglang/srt/function_call/ebnf_composer.py

@@ -165,6 +165,7 @@ class EBNFComposer:
         tool_call_separator: Optional[str] = None,
         call_rule_fmt: Optional[str] = None,
         key_value_rule_fmt: Optional[str] = None,
+        key_value_separator: str = ",",
     ):
         """
         Generalized EBNF builder for all detectors.
@@ -279,7 +280,11 @@ class EBNFComposer:
 
             # Add required properties joined by commas
             if required:
-                rule_parts.append(' "," '.join(prop_kv_pairs[k] for k in required))
+                rule_parts.append(
+                    f' "{key_value_separator}" '.join(
+                        prop_kv_pairs[k] for k in required
+                    )
+                )
 
             # Add optional properties with flexible ordering
             if optional:
@@ -292,13 +297,15 @@ class EBNFComposer:
                         if j == i:
                             opt_parts.append(prop_kv_pairs[optional[j]])
                         else:
-                            opt_parts.append(f' ( "," {prop_kv_pairs[optional[j]]} )?')
+                            opt_parts.append(
+                                f' ( "{key_value_separator}" {prop_kv_pairs[optional[j]]} )?'
+                            )
                     opt_alternatives.append("".join(opt_parts))
 
                 # Wrap with appropriate comma handling based on whether we have required properties
                 if required:
                     # Required properties exist, so optional group needs outer comma
-                    rule_parts.append(' ( "," ( ')
+                    rule_parts.append(f' ( "{key_value_separator}" ( ')
                     rule_parts.append(" | ".join(opt_alternatives))
                     rule_parts.append(" ) )?")
                 else:

python/sglang/srt/function_call/function_call_parser.py

@@ -10,6 +10,7 @@ from sglang.srt.entrypoints.openai.protocol import (
 from sglang.srt.function_call.base_format_detector import BaseFormatDetector
 from sglang.srt.function_call.core_types import ToolCallItem
 from sglang.srt.function_call.deepseekv3_detector import DeepSeekV3Detector
+from sglang.srt.function_call.glm4_moe_detector import Glm4MoeDetector
 from sglang.srt.function_call.kimik2_detector import KimiK2Detector
 from sglang.srt.function_call.llama32_detector import Llama32Detector
 from sglang.srt.function_call.mistral_detector import MistralDetector
@@ -37,6 +38,7 @@ class FunctionCallParser:
         "pythonic": PythonicDetector,
         "kimi_k2": KimiK2Detector,
         "qwen3_coder": Qwen3CoderDetector,
+        "glm45": Glm4MoeDetector,
     }
 
     def __init__(self, tools: List[Tool], tool_call_parser: str):

python/sglang/srt/function_call/glm4_moe_detector.py

+import ast
+import json
+import logging
+import re
+from typing import List
+
+from sglang.srt.entrypoints.openai.protocol import Tool
+from sglang.srt.function_call.base_format_detector import BaseFormatDetector
+from sglang.srt.function_call.core_types import (
+    StreamingParseResult,
+    StructureInfo,
+    _GetInfoFunc,
+)
+from sglang.srt.function_call.ebnf_composer import EBNFComposer
+
+logger = logging.getLogger(__name__)
+
+
+def get_argument_type(func_name: str, arg_key: str, defined_tools: list):
+    name2tool = {tool.function.name: tool for tool in defined_tools}
+    if func_name not in name2tool:
+        return None
+    tool = name2tool[func_name]
+    if arg_key not in tool.function.parameters["properties"]:
+        return None
+    return tool.function.parameters["properties"][arg_key].get("type", None)
+
+
+def parse_arguments(json_value):
+    try:
+        try:
+            parsed_value = json.loads(json_value)
+        except:
+            parsed_value = ast.literal_eval(json_value)
+        return parsed_value, True
+    except:
+        return json_value, False
+
+
+class Glm4MoeDetector(BaseFormatDetector):
+    """
+    Detector for GLM-4.5 models.
+    Assumes function call format:
+      <tool_call>get_weather\n<arg_key>city</arg_key>\n<arg_value>北京</arg_value>\n<arg_key>date</arg_key>\n<arg_value>2024-06-27</arg_value>\n</tool_call>\n<tool_call>get_weather\n<arg_key>city</arg_key>\n<arg_value>上海</arg_value>\n<arg_key>date</arg_key>\n<arg_value>2024-06-27</arg_value>\n</tool_call>
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.bot_token = "<tool_call>"
+        self.eot_token = "</tool_call>"
+        self.func_call_regex = r"<tool_call>.*?</tool_call>"
+        self.func_detail_regex = r"<tool_call>([^\n]*)\n(.*)</tool_call>"
+        self.func_arg_regex = r"<arg_key>(.*?)</arg_key>\s*<arg_value>(.*?)</arg_value>"
+
+    def has_tool_call(self, text: str) -> bool:
+        """Check if the text contains a glm-4.5 format tool call."""
+        return self.bot_token in text
+
+    def detect_and_parse(self, text: str, tools: List[Tool]) -> StreamingParseResult:
+        """
+        One-time parsing: Detects and parses tool calls in the provided text.
+
+        :param text: The complete text to parse.
+        :param tools: List of available tools.
+        :return: ParseResult indicating success or failure, consumed text, leftover text, and parsed calls.
+        """
+        idx = text.find(self.bot_token)
+        normal_text = text[:idx].strip() if idx != -1 else text
+        if self.bot_token not in text:
+            return StreamingParseResult(normal_text=normal_text, calls=[])
+        match_result_list = re.findall(self.func_call_regex, text, re.DOTALL)
+        calls = []
+        try:
+            for match_result in match_result_list:
+                # Get function name
+                func_detail = re.search(self.func_detail_regex, match_result, re.DOTALL)
+                func_name = func_detail.group(1)
+                func_args = func_detail.group(2)
+                pairs = re.findall(
+                    r"<arg_key>(.*?)</arg_key>\s*<arg_value>(.*?)</arg_value>",
+                    func_args,
+                    re.DOTALL,
+                )
+                arguments = {}
+                for arg_key, arg_value in pairs:
+                    arg_key = arg_key.strip()
+                    arg_value = arg_value.strip()
+                    arg_type = get_argument_type(func_name, arg_key, tools)
+                    if arg_type != "string":
+                        arg_value, is_good_json = parse_arguments(arg_value)
+                    arguments[arg_key] = arg_value
+                # construct match_result for parse_base_json
+                match_result = {"name": func_name, "parameters": arguments}
+                calls.extend(self.parse_base_json(match_result, tools))
+            return StreamingParseResult(normal_text=normal_text, calls=calls)
+        except Exception as e:
+            logger.error(f"Error in detect_and_parse: {e}")
+            # return the normal text if parsing fails
+            return StreamingParseResult(normal_text=text)
+
+    def parse_streaming_increment(
+        self, new_text: str, tools: List[Tool]
+    ) -> StreamingParseResult:
+        """
+        Streaming incremental parsing tool calls for GLM-4.5 format.
+        """
+        self._buffer += new_text
+        current_text = self._buffer
+
+        start = current_text.find(self.bot_token)
+        if start == -1:
+            self._buffer = ""
+            if self.current_tool_id > 0:
+                current_text = ""
+            return StreamingParseResult(normal_text=current_text)
+        # find ensures we find the first self.eot_token so there will be at most one tool_call in current_text[:end+len(self.eot_token)
+        end = current_text.find(self.eot_token)
+        if end != -1:
+            # Initialize state if this is the first tool call
+            if self.current_tool_id == -1:
+                self.current_tool_id = 0
+                self.prev_tool_call_arr = []
+                self.streamed_args_for_tool = [""]
+            # Ensure we have enough entries in our tracking arrays
+            while len(self.prev_tool_call_arr) <= self.current_tool_id:
+                self.prev_tool_call_arr.append({})
+            while len(self.streamed_args_for_tool) <= self.current_tool_id:
+                self.streamed_args_for_tool.append("")
+            result = self.detect_and_parse(
+                current_text[: end + len(self.eot_token)], tools=tools
+            )
+            if result.calls:
+                self.prev_tool_call_arr[self.current_tool_id] = {
+                    "name": result.calls[0].name,
+                    "arguments": json.loads(result.calls[0].parameters),
+                }
+                self.streamed_args_for_tool[self.current_tool_id] = result.calls[
+                    0
+                ].parameters
+                result.calls[0].tool_index = self.current_tool_id
+                self.current_tool_id += 1
+            self._buffer = current_text[end + len(self.eot_token) :]
+            return result
+        normal_text = current_text[:start]
+        self._buffer = current_text[start:]
+        return StreamingParseResult(normal_text=normal_text)
+
+    def supports_structural_tag(self) -> bool:
+        return False
+
+    def structure_info(self) -> _GetInfoFunc:
+        raise NotImplementedError()
+
+    def build_ebnf(self, tools: List[Tool]):
+        return EBNFComposer.build_ebnf(
+            tools,
+            individual_call_start_token=self.bot_token,
+            individual_call_end_token=self.eot_token,
+            # GLM4Moe is not compatible with multiple tool_calls under tool_choice condition: it will output unlimited tool_calls...
+            # tool_call_separator="\\n",
+            function_format="xml",
+            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/models/glm4_moe.py

+# Copyright 2025-2026 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-4.5 model compatible with HuggingFace weights"""
+
+import logging
+from typing import Any, Dict, Iterable, Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import PretrainedConfig
+
+from sglang.srt.distributed import (
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+    parallel_state,
+    tensor_model_parallel_all_reduce,
+)
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.amx_utils import PackWeightMethod
+from sglang.srt.layers.communicator import (
+    LayerCommunicator,
+    LayerScatterModes,
+    enable_moe_dense_fully_dp,
+)
+from sglang.srt.layers.dp_attention import (
+    get_attention_tp_rank,
+    get_attention_tp_size,
+    get_local_attention_dp_size,
+)
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.moe.ep_moe.layer import (
+    DeepEPMoE,
+    get_moe_impl_class,
+    use_flashinfer_trtllm_moe,
+)
+from sglang.srt.layers.moe.topk import TopK
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.quantization.fp8_kernel import (
+    is_fp8_fnuz,
+    per_tensor_quant_mla_fp8,
+    per_token_group_quant_mla_deep_gemm_masked_fp8,
+)
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
+from sglang.srt.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.managers.schedule_batch import global_server_args_dict
+from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.deepseek_v2 import (
+    DeepseekV2DecoderLayer,
+    DeepseekV2ForCausalLM,
+    DeepseekV2Model,
+    DeepseekV2MoE,
+)
+from sglang.srt.two_batch_overlap import (
+    MaybeTboDeepEPDispatcher,
+    model_forward_maybe_tbo,
+)
+from sglang.srt.utils import (
+    BumpAllocator,
+    DeepEPMode,
+    LazyValue,
+    add_prefix,
+    bind_or_assign,
+    cpu_has_amx_support,
+    get_bool_env_var,
+    get_device_sm,
+    get_int_env_var,
+    is_cpu,
+    is_cuda,
+    is_flashinfer_available,
+    is_hip,
+    is_non_idle_and_non_empty,
+    log_info_on_rank0,
+    use_intel_amx_backend,
+)
+
+_is_hip = is_hip()
+_is_cuda = is_cuda()
+_is_fp8_fnuz = is_fp8_fnuz()
+_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
+_is_cpu_amx_available = cpu_has_amx_support()
+_is_cpu = is_cpu()
+_device_sm = get_device_sm()
+
+if _is_cuda:
+    from sgl_kernel import dsv3_router_gemm
+elif _is_cpu and _is_cpu_amx_available:
+    pass
+
+logger = logging.getLogger(__name__)
+
+
+class Glm4MoeMLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        reduce_results: bool = True,
+        prefix: str = "",
+        tp_rank: Optional[int] = None,
+        tp_size: Optional[int] = None,
+    ) -> None:
+        super().__init__()
+        self.tp_size = tp_size
+
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=add_prefix("gate_up_proj", prefix),
+            tp_rank=tp_rank,
+            tp_size=tp_size,
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            reduce_results=reduce_results,
+            prefix=add_prefix("down_proj", prefix),
+            tp_rank=tp_rank,
+            tp_size=tp_size,
+        )
+        if hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {hidden_act}. "
+                "Only silu is supported for now."
+            )
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x, forward_batch=None, can_fuse_mlp_allreduce=False):
+        if (self.tp_size == 1) and x.shape[0] == 0:
+            return x
+
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x, can_fuse_mlp_allreduce=can_fuse_mlp_allreduce)
+        return x
+
+
+class Glm4MoeAttention(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        layer_id: int = 0,
+        rope_theta: float = 10000,
+        partial_rotary_factor: float = 0.5,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        head_dim: Optional[int] = None,
+        rms_norm_eps: float = 1e-05,
+        attention_bias: bool = True,
+        quant_config: Optional[QuantizationConfig] = None,
+        use_qk_norm: bool = False,
+        prefix: str = "",
+        alt_stream: Optional[torch.cuda.Stream] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+
+        attn_tp_rank = get_attention_tp_rank()
+        attn_tp_size = get_attention_tp_size()
+
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % attn_tp_size == 0
+        self.num_heads = self.total_num_heads // attn_tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= attn_tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % attn_tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert attn_tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // attn_tp_size)
+        self.head_dim = head_dim or hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.use_qk_norm = use_qk_norm
+        self.max_position_embeddings = max_position_embeddings
+        self.tp_rank = get_tensor_model_parallel_rank()
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=attention_bias,
+            quant_config=quant_config,
+            tp_rank=attn_tp_rank,
+            tp_size=attn_tp_size,
+            prefix=add_prefix("qkv_proj", prefix),
+        )
+
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            tp_rank=attn_tp_rank,
+            tp_size=attn_tp_size,
+            reduce_results=False,
+            prefix=add_prefix("o_proj", prefix),
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            partial_rotary_factor=partial_rotary_factor,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+        self.attn = RadixAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            layer_id=layer_id,
+            prefix=add_prefix("attn", prefix),
+        )
+
+        if self.use_qk_norm:
+            self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
+            self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
+        self.alt_stream = alt_stream
+
+    def _apply_qk_norm(
+        self, q: torch.Tensor, k: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # overlap qk norm
+        if self.alt_stream is not None and get_is_capture_mode():
+            current_stream = torch.cuda.current_stream()
+            self.alt_stream.wait_stream(current_stream)
+            q_by_head = q.reshape(-1, self.head_dim)
+            q_by_head = self.q_norm(q_by_head)
+            with torch.cuda.stream(self.alt_stream):
+                k_by_head = k.reshape(-1, self.head_dim)
+                k_by_head = self.k_norm(k_by_head)
+            current_stream.wait_stream(self.alt_stream)
+        else:
+            q_by_head = q.reshape(-1, self.head_dim)
+            q_by_head = self.q_norm(q_by_head)
+            k_by_head = k.reshape(-1, self.head_dim)
+            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 op_prepare(self, state):
+        state.attn_intermediate_state = self.forward_prepare(
+            positions=state.positions,
+            hidden_states=state.pop("hidden_states_after_comm_pre_attn"),
+            forward_batch=state.forward_batch,
+        )
+
+    def op_core(self, state):
+        state.hidden_states_after_attn = self.forward_core(
+            state.pop("attn_intermediate_state")
+        )
+
+    def forward_prepare(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ):
+        if hidden_states.shape[0] == 0:
+            return hidden_states, forward_batch, None
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        if self.use_qk_norm:
+            q, k = self._apply_qk_norm(q, k)
+        q, k = self.rotary_emb(positions, q, k)
+        inner_state = q, k, v, forward_batch
+        return None, forward_batch, inner_state
+
+    def forward_core(self, intermediate_state):
+        hidden_states, forward_batch, inner_state = intermediate_state
+        if inner_state is None:
+            return hidden_states
+        attn_output = self.attn(*inner_state)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        s = self.forward_prepare(
+            positions=positions,
+            hidden_states=hidden_states,
+            forward_batch=forward_batch,
+        )
+        return self.forward_core(s)
+
+
+class Glm4MoeGate(nn.Module):
+    def __init__(
+        self,
+        config,
+        prefix: str = "",
+        is_nextn: bool = False,
+    ):
+        super().__init__()
+        self.is_nextn = is_nextn
+        self.weight = nn.Parameter(
+            torch.empty((config.n_routed_experts, config.hidden_size))
+        )
+        self.e_score_correction_bias = nn.Parameter(
+            torch.empty((config.n_routed_experts))
+        )
+        if _is_cpu and _is_cpu_amx_available:
+            self.quant_method = PackWeightMethod(weight_names=["weight"])
+
+    def forward(self, hidden_states):
+        if use_intel_amx_backend(self):
+            return torch.ops.sgl_kernel.weight_packed_linear(
+                hidden_states,
+                self.weight,
+                None,  # bias
+                True,  # is_vnni
+            )
+
+        # NOTE: For some unknown reason, router_gemm seems degrade accept length.
+        if (
+            _is_cuda
+            and not self.is_nextn
+            and hidden_states.shape[0] < 4
+            and hidden_states.shape[1] == 7168
+            and self.weight.shape[0] == 256
+            and _device_sm >= 90
+        ):
+            logits = dsv3_router_gemm(hidden_states, self.weight).to(
+                hidden_states.dtype
+            )
+        else:
+            logits = F.linear(hidden_states, self.weight, None)
+
+        return logits
+
+
+class Glm4MoeSparseMoeBlock(DeepseekV2MoE):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        alt_stream: Optional[torch.cuda.Stream] = None,
+        is_nextn: bool = False,
+    ):
+        nn.Module.__init__(self)
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.routed_scaling_factor = config.routed_scaling_factor
+        self.n_shared_experts = config.n_shared_experts
+        self.num_fused_shared_experts = (
+            0
+            if global_server_args_dict["disable_shared_experts_fusion"]
+            else config.n_shared_experts
+        )
+        self.config = config
+        self.layer_id = layer_id
+        self.alt_stream = alt_stream
+
+        if self.tp_size > config.n_routed_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.n_routed_experts}."
+            )
+
+        if config.hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {config.hidden_act}. "
+                "Only silu is supported for now."
+            )
+
+        self.gate = Glm4MoeGate(
+            config=config, prefix=add_prefix("gate", prefix), is_nextn=is_nextn
+        )
+
+        self.topk = (
+            TopK(
+                top_k=config.num_experts_per_tok + self.num_fused_shared_experts,
+                renormalize=config.norm_topk_prob,
+                use_grouped_topk=True,
+                num_expert_group=config.n_group,
+                num_fused_shared_experts=self.num_fused_shared_experts,
+                topk_group=config.topk_group,
+                correction_bias=self.gate.e_score_correction_bias,
+                routed_scaling_factor=self.routed_scaling_factor,
+            )
+            if not use_flashinfer_trtllm_moe
+            else None
+        )
+
+        self.experts = get_moe_impl_class()(
+            num_experts=config.n_routed_experts
+            + self.num_fused_shared_experts
+            + global_server_args_dict["ep_num_redundant_experts"],
+            top_k=config.num_experts_per_tok + self.num_fused_shared_experts,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            layer_id=self.layer_id,
+            quant_config=quant_config,
+            routed_scaling_factor=self.routed_scaling_factor,
+            prefix=add_prefix("experts", prefix),
+            **(
+                dict(deepep_mode=DeepEPMode[global_server_args_dict["deepep_mode"]])
+                if global_server_args_dict["enable_deepep_moe"]
+                else {}
+            ),
+            # Additional args for FusedMoE
+            **(
+                dict(
+                    enable_flashinfer_cutlass_moe=True,
+                    enable_ep_moe=global_server_args_dict["enable_ep_moe"],
+                )
+                if global_server_args_dict["enable_flashinfer_cutlass_moe"]
+                else {}
+            ),
+            **(
+                dict(
+                    renormalize=config.norm_topk_prob,
+                    use_grouped_topk=True,
+                    num_expert_group=config.n_group,
+                    num_fused_shared_experts=self.num_fused_shared_experts,
+                    topk_group=config.topk_group,
+                    correction_bias=self.gate.e_score_correction_bias,
+                )
+                if use_flashinfer_trtllm_moe
+                else {}
+            ),
+        )
+
+        self.shared_experts_is_int8 = False
+        self.shared_experts_is_fp8 = False
+        # self.shared_experts_weight_block_size = None
+        if config.n_shared_experts is not None and self.num_fused_shared_experts == 0:
+            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
+            self.shared_experts = Glm4MoeMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                reduce_results=False,
+                prefix=add_prefix("shared_experts", prefix),
+                **(
+                    dict(tp_rank=0, tp_size=1)
+                    if global_server_args_dict["enable_deepep_moe"]
+                    else {}
+                ),
+            )
+            is_packed_weight = hasattr(
+                self.shared_experts.gate_up_proj.quant_method, "quant_config"
+            )
+            self.shared_experts_is_int8 = (
+                not is_packed_weight
+                and self.shared_experts.gate_up_proj.weight.dtype == torch.int8
+            )
+            self.shared_experts_is_fp8 = (
+                not is_packed_weight
+                and self.shared_experts.gate_up_proj.weight.dtype == torch.float8_e4m3fn
+            )
+
+        self.top_k = config.num_experts_per_tok
+
+        if global_server_args_dict["enable_deepep_moe"]:
+            # TODO: we will support tp < ep in the future
+            self.ep_size = get_tensor_model_parallel_world_size()
+            self.num_experts = (
+                config.n_routed_experts
+                + global_server_args_dict["ep_num_redundant_experts"]
+            )
+            self.renormalize = config.norm_topk_prob
+            self.topk_group = config.topk_group
+            self.num_expert_group = config.n_group
+            self.correction_bias = (
+                self.gate.e_score_correction_bias.data
+                if self.gate.e_score_correction_bias is not None
+                else None
+            )
+
+            self.deepep_dispatcher = MaybeTboDeepEPDispatcher(
+                group=parallel_state.get_tp_group().device_group,
+                router_topk=self.top_k,
+                permute_fusion=True,
+                num_experts=self.num_experts,
+                num_local_experts=config.n_routed_experts // self.tp_size,
+                hidden_size=config.hidden_size,
+                params_dtype=config.torch_dtype,
+                deepep_mode=DeepEPMode[global_server_args_dict["deepep_mode"]],
+                async_finish=True,
+                return_recv_hook=True,
+            )
+
+        self._enable_deepep_moe = global_server_args_dict["enable_deepep_moe"]
+
+
+class Glm4MoeDecoderLayer(DeepseekV2DecoderLayer):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        is_nextn: bool = False,
+        prefix: str = "",
+        alt_stream: Optional[torch.cuda.Stream] = None,
+    ) -> None:
+        nn.Module.__init__(self)
+        self.hidden_size = config.hidden_size
+        self.config = config
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        partial_rotary_factor = getattr(config, "partial_rotary_factor", 0.5)
+        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
+        head_dim = getattr(
+            config, "head_dim", config.hidden_size // config.num_attention_heads
+        )
+        rms_norm_eps = config.rms_norm_eps
+        attention_bias = config.attention_bias
+        self.enable_dp_attention = global_server_args_dict["enable_dp_attention"]
+        self.layer_id = layer_id
+        self.self_attn = Glm4MoeAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            layer_id=layer_id,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            partial_rotary_factor=partial_rotary_factor,
+            max_position_embeddings=max_position_embeddings,
+            head_dim=head_dim,
+            rms_norm_eps=rms_norm_eps,
+            attention_bias=attention_bias,
+            quant_config=quant_config,
+            prefix=add_prefix("self_attn", prefix),
+            use_qk_norm=config.use_qk_norm,
+        )
+
+        self.is_layer_sparse = self._is_layer_sparse(layer_id, is_nextn=is_nextn)
+        is_previous_layer_sparse = self._is_layer_sparse(layer_id - 1, is_nextn=False)
+
+        num_layers = 1 if is_nextn else config.num_hidden_layers
+        self.layer_scatter_modes = LayerScatterModes.init_new(
+            layer_id=layer_id,
+            num_layers=num_layers,
+            is_layer_sparse=self.is_layer_sparse,
+            is_previous_layer_sparse=is_previous_layer_sparse,
+        )
+
+        if self.is_layer_sparse:
+            self.mlp = Glm4MoeSparseMoeBlock(
+                config=config,
+                quant_config=quant_config,
+                prefix=add_prefix("mlp", prefix),
+                layer_id=self.layer_id,
+            )
+        else:
+            if enable_moe_dense_fully_dp():
+                mlp_tp_rank, mlp_tp_size = 0, 1
+            else:
+                mlp_tp_rank, mlp_tp_size = None, None
+            self.mlp = Glm4MoeMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                prefix=add_prefix("mlp", prefix),
+                tp_rank=mlp_tp_rank,
+                tp_size=mlp_tp_size,
+            )
+
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+        self.layer_communicator = LayerCommunicator(
+            layer_scatter_modes=self.layer_scatter_modes,
+            input_layernorm=self.input_layernorm,
+            post_attention_layernorm=self.post_attention_layernorm,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+        residual: Optional[torch.Tensor],
+        zero_allocator: BumpAllocator,
+    ) -> torch.Tensor:
+        hidden_states, residual = self.layer_communicator.prepare_attn(
+            hidden_states, residual, forward_batch
+        )
+
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            forward_batch=forward_batch,
+        )
+
+        hidden_states, residual = self.layer_communicator.prepare_mlp(
+            hidden_states, residual, forward_batch
+        )
+
+        hidden_states = self.mlp(hidden_states, forward_batch)
+
+        hidden_states, residual = self.layer_communicator.postprocess_layer(
+            hidden_states, residual, forward_batch
+        )
+
+        return hidden_states, residual
+
+
+class Glm4MoeModel(DeepseekV2Model):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        nn.Module.__init__(self)
+        self.padding_id = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.first_k_dense_replace = config.first_k_dense_replace
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            enable_tp=not global_server_args_dict["enable_dp_attention"],
+        )
+        self.alt_stream = torch.cuda.Stream() if _is_cuda else None
+        self.layers = nn.ModuleList(
+            [
+                Glm4MoeDecoderLayer(
+                    config,
+                    layer_id,
+                    quant_config=quant_config,
+                    prefix=add_prefix(f"layers.{layer_id}", prefix),
+                    alt_stream=self.alt_stream,
+                )
+                for layer_id in range(config.num_hidden_layers)
+            ]
+        )
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.dp_size = get_local_attention_dp_size()
+
+
+class Glm4MoeForCausalLM(DeepseekV2ForCausalLM):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        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.quant_config = quant_config
+        self.determine_num_fused_shared_experts("Glm4MoeForCausalLM")
+        self.model = Glm4MoeModel(
+            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("lm_head", prefix),
+            use_attn_tp_group=global_server_args_dict["enable_dp_lm_head"],
+        )
+        self.logits_processor = LogitsProcessor(config)
+        self.dp_size = get_local_attention_dp_size()
+
+        self._routed_experts_weights_of_layer = LazyValue(
+            lambda: {
+                layer_id: layer.mlp.get_moe_weights()
+                for layer_id, layer in enumerate(self.model.layers)
+                if isinstance(layer.mlp, DeepseekV2MoE)
+            }
+        )
+
+    def determine_num_fused_shared_experts(
+        self, architecture: str = "DeepseekV3ForCausalLM"
+    ):
+        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_routed_experts != 128
+            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 (
+            global_server_args_dict["enable_deepep_moe"]
+            or global_server_args_dict["enable_ep_moe"]
+        ):
+            disable_reason = "Deepseek GLM-4.5 can not use shared experts fusion optimization when in deepep_moe or ep_moe mode."
+
+        if disable_reason is not None:
+            global_server_args_dict["disable_shared_experts_fusion"] = True
+            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 get_input_embeddings(self) -> nn.Embedding:
+        return self.model.embed_tokens
+
+    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"
+                ):
+                    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 "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:
+                    # 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 = [Glm4MoeForCausalLM]

python/sglang/srt/models/glm4_moe_nextn.py

+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Inference-only GLM-4.5 NextN 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.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.managers.schedule_batch import global_server_args_dict
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.models.glm4_moe import Glm4MoeDecoderLayer, Glm4MoeForCausalLM
+from sglang.srt.utils import BumpAllocator, add_prefix
+
+logger = logging.getLogger(__name__)
+
+
+class Glm4MoeModelNextN(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 Glm4MoeForCausalLMNextN quant config for modelopt_fp4 GLM-4.5 model."
+            )
+            quant_config = None
+
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            enable_tp=not global_server_args_dict["enable_dp_attention"],
+            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 = Glm4MoeDecoderLayer(
+            config,
+            0,
+            quant_config=quant_config,
+            is_nextn=True,
+            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:
+        zero_allocator = BumpAllocator(
+            buffer_size=2,
+            dtype=torch.float32,
+            device=(
+                input_embeds.device if input_embeds is not None else input_ids.device
+            ),
+        )
+
+        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, zero_allocator
+            )
+
+        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 Glm4MoeForCausalLMNextN(Glm4MoeForCausalLM):
+
+    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.determine_num_fused_shared_experts("Glm4MoeForCausalLMNextN")
+
+        self.model = Glm4MoeModelNextN(
+            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=global_server_args_dict["enable_dp_lm_head"],
+        )
+        self.logits_processor = LogitsProcessor(config)
+
+    @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 = [Glm4MoeForCausalLMNextN]

python/sglang/srt/reasoning_parser.py

@@ -231,6 +231,7 @@ class ReasoningParser:
         "deepseek-r1": DeepSeekR1Detector,
         "qwen3": Qwen3Detector,
         "qwen3-thinking": Qwen3ThinkingDetector,
+        "glm45": Qwen3Detector,
         "kimi": KimiDetector,
     }
 

python/sglang/srt/server_args.py

@@ -513,7 +513,7 @@ class ServerArgs:
                 )
 
             model_arch = self.get_hf_config().architectures[0]
-            if model_arch == "DeepseekV3ForCausalLM":
+            if model_arch in ["DeepseekV3ForCausalLM", "Glm4MoeForCausalLM"]:
                 # Auto set draft_model_path DeepSeek-V3/R1
                 if self.speculative_draft_model_path is None:
                     self.speculative_draft_model_path = self.model_path
@@ -1108,6 +1108,7 @@ class ServerArgs:
                 "pythonic",
                 "kimi_k2",
                 "qwen3_coder",
+                "glm45",
             ],
             default=ServerArgs.tool_call_parser,
             help="Specify the parser for handling tool-call interactions. Options include: 'qwen25', 'mistral', 'llama3', 'deepseekv3', 'pythonic', 'kimi_k2', and 'qwen3_coder'.",

python/sglang/srt/utils.py

@@ -2343,6 +2343,7 @@ def is_fa3_default_architecture(hf_config):
         "Gemma3ForConditionalGeneration",
         "Qwen3ForCausalLM",
         "Qwen3MoeForCausalLM",
+        "Glm4MoeForCausalLM",
     }
     return architectures[0] in default_archs
 

test/srt/openai_server/features/test_enable_thinking.py

@@ -43,6 +43,7 @@ class TestEnableThinking(CustomTestCase):
                 "qwen3",
             ],
         )
+        cls.additional_chat_kwargs = {}
 
     @classmethod
     def tearDownClass(cls):
@@ -59,6 +60,7 @@ class TestEnableThinking(CustomTestCase):
                 "temperature": 0,
                 "separate_reasoning": True,
                 "chat_template_kwargs": {"enable_thinking": True},
+                **self.additional_chat_kwargs,
             },
         )
 
@@ -82,6 +84,7 @@ class TestEnableThinking(CustomTestCase):
                 "temperature": 0,
                 "separate_reasoning": True,
                 "chat_template_kwargs": {"enable_thinking": False},
+                **self.additional_chat_kwargs,
             },
         )
 
@@ -107,6 +110,7 @@ class TestEnableThinking(CustomTestCase):
                 "separate_reasoning": True,
                 "stream": True,
                 "chat_template_kwargs": {"enable_thinking": True},
+                **self.additional_chat_kwargs,
             },
             stream=True,
         )
@@ -151,6 +155,7 @@ class TestEnableThinking(CustomTestCase):
                 "separate_reasoning": True,
                 "stream": True,
                 "chat_template_kwargs": {"enable_thinking": False},
+                **self.additional_chat_kwargs,
             },
             stream=True,
         )
@@ -184,5 +189,55 @@ class TestEnableThinking(CustomTestCase):
         )
 
 
+## Skip for ci test
+# class TestGLM45EnableThinking(TestEnableThinking):
+#     @classmethod
+#     def setUpClass(cls):
+#         # Replace with the model name needed for testing; if not required, reuse DEFAULT_SMALL_MODEL_NAME_FOR_TEST
+#         cls.model = "THUDM/GLM-4.5"
+#         cls.base_url = DEFAULT_URL_FOR_TEST
+#         cls.api_key = "sk-1234"
+#         cls.process = popen_launch_server(
+#             cls.model,
+#             cls.base_url,
+#             timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+#             api_key=cls.api_key,
+#             other_args=[
+#                 "--tool-call-parser",
+#                 "glm45",
+#                 "--reasoning-parser",
+#                 "glm45",
+#                 "--tp-size",
+#                 "8"
+#             ],
+#         )
+
+#         # Validate whether enable-thinking conflict with tool_calls
+#         cls.additional_chat_kwargs = {
+#             "tools": [
+#                 {
+#                     "type": "function",
+#                     "function": {
+#                         "name": "add",
+#                         "description": "Compute the sum of two numbers",
+#                         "parameters": {
+#                             "type": "object",
+#                             "properties": {
+#                                 "a": {
+#                                     "type": "int",
+#                                     "description": "A number",
+#                                 },
+#                                 "b": {
+#                                     "type": "int",
+#                                     "description": "A number",
+#                                 },
+#                             },
+#                             "required": ["a", "b"],
+#                         },
+#                     },
+#                 }
+#             ]
+#         }
+
 if __name__ == "__main__":
     unittest.main()

test/srt/openai_server/function_call/test_openai_function_calling.py

@@ -223,7 +223,10 @@ class TestOpenAIServerFunctionCalling(CustomTestCase):
 
         messages = [
             {"role": "system", "content": self.SYSTEM_MESSAGE},
-            {"role": "user", "content": "What is the temperature in Paris?"},
+            {
+                "role": "user",
+                "content": "What is the temperature in Paris in celsius??",
+            },
         ]
 
         response_stream = client.chat.completions.create(
@@ -910,5 +913,40 @@ class TestOpenAIPythonicFunctionCalling(CustomTestCase):
         )
 
 
+## Skip for ci test
+# class TestGLM45ServerFunctionCalling(TestOpenAIServerFunctionCalling):
+#     @classmethod
+#     def setUpClass(cls):
+#         # Replace with the model name needed for testing; if not required, reuse DEFAULT_SMALL_MODEL_NAME_FOR_TEST
+#         cls.model = "THUDM/GLM-4.5"
+#         cls.base_url = DEFAULT_URL_FOR_TEST
+#         cls.api_key = "sk-123456"
+
+#         # Start the local OpenAI Server. If necessary, you can add other parameters such as --enable-tools.
+#         cls.process = popen_launch_server(
+#             cls.model,
+#             cls.base_url,
+#             timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+#             api_key=cls.api_key,
+#             other_args=[
+#                 # If your server needs extra parameters to test function calling, please add them here.
+#                 "--tool-call-parser",
+#                 "glm45",
+#                 "--reasoning-parser",
+#                 "glm45",
+#                 "--tp-size",
+#                 "8"
+#             ],
+#         )
+#         cls.base_url += "/v1"
+#         cls.tokenizer = get_tokenizer(cls.model)
+
+#     # This test is too difficult for GLM4-moe. Skip it from the UT
+#     def test_function_call_required(self):
+#         pass
+
+#     def test_function_calling_multiturn(self):
+#         self._test_function_calling_multiturn()
+
 if __name__ == "__main__":
     unittest.main()

test/srt/test_function_call_parser.py

@@ -6,6 +6,7 @@ from xgrammar import GrammarCompiler, TokenizerInfo
 from sglang.srt.entrypoints.openai.protocol import Function, Tool
 from sglang.srt.function_call.base_format_detector import BaseFormatDetector
 from sglang.srt.function_call.deepseekv3_detector import DeepSeekV3Detector
+from sglang.srt.function_call.glm4_moe_detector import Glm4MoeDetector
 from sglang.srt.function_call.kimik2_detector import KimiK2Detector
 from sglang.srt.function_call.llama32_detector import Llama32Detector
 from sglang.srt.function_call.mistral_detector import MistralDetector
@@ -510,6 +511,7 @@ class TestEBNFGeneration(unittest.TestCase):
         self.qwen25_detector = Qwen25Detector()
         self.qwen3_coder_detector = Qwen3CoderDetector()
         self.kimik2_detector = KimiK2Detector()
+        self.glm45_detector = Glm4MoeDetector()
 
     def test_pythonic_detector_ebnf(self):
         """Test that the PythonicDetector generates valid EBNF."""
@@ -622,6 +624,29 @@ class TestEBNFGeneration(unittest.TestCase):
         except RuntimeError as e:
             self.fail(f"Failed to compile EBNF: {e}")
 
+    def test_glm45_detector_ebnf(self):
+        """Test that the Glm4MoeDetector generates valid EBNF."""
+        ebnf = self.glm45_detector.build_ebnf(self.tools)
+        self.assertIsNotNone(ebnf)
+        # Check that the EBNF contains expected patterns for XML format
+        self.assertIn('"<tool_call>" function_call "</tool_call>"', ebnf)
+        self.assertIn('"get_weather" "\\n" arguments_get_weather', ebnf)
+        self.assertIn(
+            '"<arg_key>location</arg_key>" "\\n" "<arg_value>" xml_text "</arg_value>" ( "\\n" ( "<arg_key>unit</arg_key>" "\\n" "<arg_value>" ("celsius" | "fahrenheit") "</arg_value>" ) )?',
+            ebnf,
+        )
+        self.assertIn('"search" "\\n" arguments_search', ebnf)
+        self.assertIn(
+            '"<arg_key>query</arg_key>" "\\n" "<arg_value>" xml_text "</arg_value>"',
+            ebnf,
+        )
+        # Validate that the EBNF can be compiled by GrammarCompiler
+        try:
+            ctx = self.grammar_compiler.compile_grammar(ebnf)
+            self.assertIsNotNone(ctx, "EBNF should be valid and compile successfully")
+        except RuntimeError as e:
+            self.fail(f"Failed to compile EBNF: {e}")
+
     def test_qwen3_coder_detector_ebnf(self):
         """Test that the Qwen3CoderDetector generates valid EBNF."""
         ebnf = self.qwen3_coder_detector.build_ebnf(self.tools)
@@ -1919,5 +1944,164 @@ circle
         self.assertEqual(params2["dimensions"], {"radius": 5})
 
 
+class TestGlm4MoeDetector(unittest.TestCase):
+    def setUp(self):
+        self.tools = [
+            Tool(
+                type="function",
+                function=Function(
+                    name="get_weather",
+                    description="Get weather information",
+                    parameters={
+                        "type": "object",
+                        "properties": {
+                            "city": {"type": "string", "description": "City name"},
+                            "date": {"type": "string", "description": "Date"},
+                        },
+                        "required": ["city", "date"],
+                    },
+                ),
+            ),
+        ]
+        self.detector = Glm4MoeDetector()
+
+    def test_single_tool_call(self):
+        text = (
+            "<tool_call>get_weather\n"
+            "<arg_key>city</arg_key>\n<arg_value>Beijing</arg_value>\n"
+            "<arg_key>date</arg_key>\n<arg_value>2024-06-27</arg_value>\n"
+            "</tool_call>"
+        )
+        result = self.detector.detect_and_parse(text, self.tools)
+        self.assertEqual(len(result.calls), 1)
+        self.assertEqual(result.calls[0].name, "get_weather")
+        self.assertEqual(
+            result.calls[0].parameters, '{"city": "Beijing", "date": "2024-06-27"}'
+        )
+        self.assertEqual(result.normal_text, "")
+
+    def test_multiple_tool_calls(self):
+        text = (
+            "<tool_call>get_weather\n"
+            "<arg_key>city</arg_key>\n<arg_value>Beijing</arg_value>\n"
+            "<arg_key>date</arg_key>\n<arg_value>2024-06-27</arg_value>\n"
+            "</tool_call>"
+            "<tool_call>get_weather\n"
+            "<arg_key>city</arg_key>\n<arg_value>Shanghai</arg_value>\n"
+            "<arg_key>date</arg_key>\n<arg_value>2024-06-28</arg_value>\n"
+            "</tool_call>"
+        )
+        result = self.detector.detect_and_parse(text, self.tools)
+        self.assertEqual(len(result.calls), 2)
+        self.assertEqual(result.calls[0].name, "get_weather")
+        self.assertEqual(
+            result.calls[0].parameters, '{"city": "Beijing", "date": "2024-06-27"}'
+        )
+        self.assertEqual(result.calls[1].name, "get_weather")
+        self.assertEqual(
+            result.calls[1].parameters, '{"city": "Shanghai", "date": "2024-06-28"}'
+        )
+        self.assertEqual(result.normal_text, "")
+
+    def test_streaming_tool_call(self):
+        """Test streaming incremental parsing of a tool call."""
+        chunks = [
+            "<tool_call>get_weather\n",
+            "<arg_key>city</arg_key>\n<arg_value>Beijing</arg_value>\n",
+            "<arg_key>date</arg_key>\n<arg_value>2024-06-27</arg_value>\n",
+            "</tool_call>",
+        ]
+        tool_calls = []
+        for chunk in chunks:
+            result = self.detector.parse_streaming_increment(chunk, self.tools)
+            for tool_call_chunk in result.calls:
+                if (
+                    hasattr(tool_call_chunk, "tool_index")
+                    and tool_call_chunk.tool_index is not None
+                ):
+                    while len(tool_calls) <= tool_call_chunk.tool_index:
+                        tool_calls.append({"name": "", "parameters": {}})
+                    tc = tool_calls[tool_call_chunk.tool_index]
+                    if tool_call_chunk.name:
+                        tc["name"] = tool_call_chunk.name
+                    if tool_call_chunk.parameters:
+                        tc["parameters"] = tool_call_chunk.parameters
+        self.assertEqual(len(tool_calls), 1)
+        self.assertEqual(tool_calls[0]["name"], "get_weather")
+        self.assertEqual(
+            tool_calls[0]["parameters"], '{"city": "Beijing", "date": "2024-06-27"}'
+        )
+
+    def test_streaming_multiple_tool_calls(self):
+        """Test streaming incremental parsing of multiple tool calls."""
+        chunks = [
+            "<tool_call>get_weather\n",
+            "<arg_key>city</arg_key>\n<arg_value>Beijing</arg_value>\n",
+            "<arg_key>date</arg_key>\n<arg_value>2024-06-27</arg_value>\n",
+            "</tool_call><tool_call>get_weather\n",
+            "<arg_key>city</arg_key>\n<arg_value>Shanghai</arg_value>\n",
+            "<arg_key>date</arg_key>\n<arg_value>2024-06-28</arg_value>\n",
+            "</tool_call>",
+        ]
+        tool_calls = []
+        for chunk in chunks:
+            result = self.detector.parse_streaming_increment(chunk, self.tools)
+            for tool_call_chunk in result.calls:
+                if (
+                    hasattr(tool_call_chunk, "tool_index")
+                    and tool_call_chunk.tool_index is not None
+                ):
+                    while len(tool_calls) <= tool_call_chunk.tool_index:
+                        tool_calls.append({"name": "", "parameters": {}})
+                    tc = tool_calls[tool_call_chunk.tool_index]
+                    if tool_call_chunk.name:
+                        tc["name"] = tool_call_chunk.name
+                    if tool_call_chunk.parameters:
+                        tc["parameters"] = tool_call_chunk.parameters
+        self.assertEqual(len(tool_calls), 2)
+        self.assertEqual(tool_calls[0]["name"], "get_weather")
+        self.assertEqual(
+            tool_calls[0]["parameters"], '{"city": "Beijing", "date": "2024-06-27"}'
+        )
+        self.assertEqual(tool_calls[1]["name"], "get_weather")
+        self.assertEqual(
+            tool_calls[1]["parameters"], '{"city": "Shanghai", "date": "2024-06-28"}'
+        )
+
+    def test_tool_call_completion(self):
+        """Test that the buffer and state are reset after a tool call is completed."""
+        chunks = [
+            "<tool_call>get_weather\n",
+            "<arg_key>city</arg_key>\n<arg_value>Beijing</arg_value>\n",
+            "<arg_key>date</arg_key>\n<arg_value>2024-06-27</arg_value>\n",
+            "</tool_call>",
+        ]
+        for chunk in chunks:
+            result = self.detector.parse_streaming_increment(chunk, self.tools)
+        self.assertEqual(self.detector.current_tool_id, 1)
+
+    def test_invalid_tool_call(self):
+        """Test that invalid tool calls are handled correctly."""
+        text = "<tool_call>invalid_func\n<arg_key>city</arg_key>\n<arg_value>Beijing</arg_value>\n</tool_call>"
+        result = self.detector.detect_and_parse(text, self.tools)
+        self.assertEqual(len(result.calls), 0)
+
+    def test_partial_tool_call(self):
+        """Test parsing a partial tool call that spans multiple chunks."""
+        text1 = "<tool_call>get_weather\n<arg_key>city</arg_key>\n"
+        result1 = self.detector.parse_streaming_increment(text1, self.tools)
+        self.assertEqual(result1.normal_text, "")
+        self.assertEqual(result1.calls, [])
+        self.assertEqual(self.detector._buffer, text1)
+        text2 = "<arg_value>Beijing</arg_value>\n<arg_key>date</arg_key>\n<arg_value>2024-06-27</arg_value>\n</tool_call>"
+        result2 = self.detector.parse_streaming_increment(text2, self.tools)
+        self.assertEqual(len(result2.calls), 1)
+        self.assertEqual(result2.calls[0].name, "get_weather")
+        self.assertEqual(
+            result2.calls[0].parameters, '{"city": "Beijing", "date": "2024-06-27"}'
+        )
+        self.assertEqual(self.detector._buffer, "")
+
+
 if __name__ == "__main__":
     unittest.main()