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>

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

@@ -0,0 +1,165 @@
+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_nextn.py

@@ -0,0 +1,167 @@
+# 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