[New Model] GLM4.7-Flash (#17247)

Co-authored-by: zRzRzRzRzRzRzR <2448370773@qq.com>
Co-authored-by: JustinTong0323 <justinning0323@gmail.com>
Co-authored-by: Xinyuan Tong <xinyuantong.cs@gmail.com>
Co-authored-by: Baizhou Zhang <sobereddiezhang@gmail.com>

python/sglang/srt/configs/model_config.py

@@ -269,7 +269,10 @@ class ModelConfig:
         ):
             self.hf_config.architectures[0] = "DeepseekV3ForCausalLMNextN"
 
-        if is_draft_model and self.hf_config.architectures[0] == "Glm4MoeForCausalLM":
+        if is_draft_model and self.hf_config.architectures[0] in [
+            "Glm4MoeForCausalLM",
+            "Glm4MoeLiteForCausalLM",
+        ]:
             self.hf_config.architectures[0] = "Glm4MoeForCausalLMNextN"
 
         if (
@@ -375,6 +378,7 @@ class ModelConfig:
             or "DeepseekV32ForCausalLM" in self.hf_config.architectures
             or "DeepseekV3ForCausalLM" in self.hf_config.architectures
             or "DeepseekV3ForCausalLMNextN" in self.hf_config.architectures
+            or "Glm4MoeLiteForCausalLM" in self.hf_config.architectures
             or "LongcatFlashForCausalLM" in self.hf_config.architectures
             or "LongcatFlashForCausalLMNextN" in self.hf_config.architectures
             or "DotsVLMForCausalLM" in self.hf_config.architectures
@@ -394,8 +398,14 @@ class ModelConfig:
                 else None
             )
 
+            if "Glm4MoeLiteForCausalLM" in self.hf_config.architectures:
+                self.scaling = 1
+                self.hf_config.rope_scaling = None
+            else:
                 # Handle rope scaling with yarn
-            self.scaling = 1 / math.sqrt(self.qk_nope_head_dim + self.qk_rope_head_dim)
+                self.scaling = 1 / math.sqrt(
+                    self.qk_nope_head_dim + self.qk_rope_head_dim
+                )
                 if self.hf_config.rope_scaling:
                     mscale_all_dim = self.hf_config.rope_scaling.get(
                         "mscale_all_dim", False

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

@@ -454,6 +454,7 @@ class OpenAIServingChat(OpenAIServingBase):
                         if request.chat_template_kwargs
                         else {}
                     ),
+                    return_dict=False,
                 )
             except Exception as e:
                 # If the first attempt fails, try transforming the tools format
@@ -476,6 +477,7 @@ class OpenAIServingChat(OpenAIServingBase):
                             if request.chat_template_kwargs
                             else {}
                         ),
+                        return_dict=False,
                     )
                 except jinja2.TemplateError as template_error:
                     # Template errors (e.g., from raise_exception in Jinja templates)

python/sglang/srt/models/deepseek_common/attention_forward_methods/forward_mha.py

@@ -17,7 +17,7 @@ from sglang.srt.models.deepseek_common.utils import (
     _use_aiter_gfx95,
 )
 from sglang.srt.server_args import get_global_server_args
-from sglang.srt.utils import BumpAllocator
+from sglang.srt.utils import BumpAllocator, next_power_of_2
 
 if TYPE_CHECKING:
     from sglang.srt.models.deepseek_v2 import DeepseekV2AttentionMLA
@@ -472,7 +472,12 @@ class DeepseekMHAForwardMixin:
         ):
             k = k_nope.new_empty(*k_shape)
             concat_mla_k(k=k, k_nope=k_nope, k_rope=k_pe)
-        elif _is_cuda:
+        elif (
+            _is_cuda
+            and next_power_of_2(self.num_local_heads) == self.num_local_heads
+            and next_power_of_2(self.qk_nope_head_dim) == self.qk_nope_head_dim
+            and next_power_of_2(self.qk_rope_head_dim) == self.qk_rope_head_dim
+        ):
             # fa3 mha support fp8 inputs
             if (
                 self.current_attention_backend == "fa3"

python/sglang/srt/models/glm4_moe.py

@@ -685,6 +685,8 @@ class Glm4MoeDecoderLayer(nn.Module):
         attention_bias = config.attention_bias
         self.layer_id = layer_id
 
+        use_qk_norm = config.use_qk_norm if hasattr(config, "use_qk_norm") else False
+
         self.self_attn = Glm4MoeAttention(
             hidden_size=self.hidden_size,
             num_heads=config.num_attention_heads,
@@ -699,7 +701,7 @@ class Glm4MoeDecoderLayer(nn.Module):
             attention_bias=attention_bias,
             quant_config=quant_config,
             prefix=add_prefix("self_attn", prefix),
-            use_qk_norm=config.use_qk_norm,
+            use_qk_norm=use_qk_norm,
             alt_stream=alt_stream,
         )
 

python/sglang/srt/models/glm4_moe_lite.py

@@ -0,0 +1,808 @@
+# 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-Lite model compatible with HuggingFace weights"""
+
+import logging
+from typing import Iterable, Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import PretrainedConfig
+
+from sglang.srt.batch_overlap.single_batch_overlap import SboFlags
+from sglang.srt.distributed import (
+    get_moe_expert_parallel_world_size,
+    get_pp_group,
+    get_tensor_model_parallel_world_size,
+)
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.communicator import (
+    LayerCommunicator,
+    LayerScatterModes,
+    enable_moe_dense_fully_dp,
+)
+from sglang.srt.layers.dp_attention import (
+    get_attention_tp_size,
+    is_dp_attention_enabled,
+)
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import MergedColumnParallelLinear, RowParallelLinear
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.moe import (
+    get_moe_a2a_backend,
+    should_use_flashinfer_cutlass_moe_fp4_allgather,
+)
+from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class
+from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
+from sglang.srt.layers.moe.topk import TopK, TopKOutputFormat
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.utils import PPMissingLayer
+from sglang.srt.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.deepseek_v2 import (
+    DeepseekV2AttentionMLA,
+    DeepseekV2DecoderLayer,
+    DeepseekV2ForCausalLM,
+    DeepseekV2Model,
+    DeepseekV2MoE,
+)
+from sglang.srt.server_args import get_global_server_args
+from sglang.srt.utils import (
+    BumpAllocator,
+    LazyValue,
+    add_prefix,
+    get_device_sm,
+    is_cuda,
+    log_info_on_rank0,
+    make_layers,
+)
+
+_is_cuda = is_cuda()
+_device_sm = get_device_sm()
+
+logger = logging.getLogger(__name__)
+
+
+class Glm4MoeLiteMLP(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,
+        should_allreduce_fusion: bool = False,
+        use_reduce_scatter: bool = False,
+        gemm_output_zero_allocator: BumpAllocator = None,
+    ):
+        # Keep parity with DeepseekV2MLP.forward signature since DeepseekV2DecoderLayer
+        # invokes MLP modules with these extra arguments.
+        if (self.tp_size == 1) and x.shape[0] == 0:
+            return x
+
+        # Some quantization wrappers store the underlying parameter as `weight_packed`.
+        if not hasattr(self.gate_up_proj, "weight"):
+            self.gate_up_proj.weight = getattr(self.gate_up_proj, "weight_packed")
+        if not hasattr(self.down_proj, "weight"):
+            self.down_proj.weight = getattr(self.down_proj, "weight_packed")
+
+        if (
+            gemm_output_zero_allocator is not None
+            and x.shape[0] <= 256
+            and self.gate_up_proj.weight.dtype == torch.uint8
+        ):
+            y = gemm_output_zero_allocator.allocate(
+                x.shape[0] * self.gate_up_proj.output_size_per_partition
+            ).view(x.shape[0], self.gate_up_proj.output_size_per_partition)
+            x = (x, None, y)
+
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(
+            x,
+            skip_all_reduce=should_allreduce_fusion or use_reduce_scatter,
+        )
+        return x
+
+
+class Glm4MoeLiteGate(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), dtype=torch.float32)
+        )
+
+    def forward(self, hidden_states, gemm_output_zero_allocator: BumpAllocator = None):
+        # 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
+        ):
+            from sgl_kernel import dsv3_router_gemm
+
+            logits = dsv3_router_gemm(hidden_states, self.weight).to(
+                hidden_states.dtype
+            )
+        else:
+            logits = F.linear(hidden_states, self.weight, None)
+
+        return logits
+
+
+class Glm4MoeLiteSparseMoeBlock(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 get_global_server_args().disable_shared_experts_fusion
+            else config.n_shared_experts
+        )
+        self.config = config
+        self.layer_id = layer_id
+        self.alt_stream = alt_stream
+        self.is_nextn = is_nextn
+
+        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 = Glm4MoeLiteGate(
+            config=config, prefix=add_prefix("gate", prefix), is_nextn=is_nextn
+        )
+
+        self.experts = get_moe_impl_class(quant_config)(
+            num_experts=config.n_routed_experts
+            + self.num_fused_shared_experts
+            + get_global_server_args().ep_num_redundant_experts,
+            num_fused_shared_experts=self.num_fused_shared_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),
+        )
+
+        self.topk = TopK(
+            top_k=config.num_experts_per_tok + self.num_fused_shared_experts,
+            layer_id=self.layer_id,
+            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,
+            quant_config=quant_config,
+            routed_scaling_factor=self.routed_scaling_factor,
+            apply_routed_scaling_factor_on_output=self.experts.should_fuse_routed_scaling_factor_in_topk,
+            # Some Fp4 MoE backends require the output format to be bypassed but the MTP layers are unquantized
+            # and requires the output format to be standard. We use quant_config to determine the output format.
+            output_format=TopKOutputFormat.STANDARD if quant_config is None else None,
+        )
+
+        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
+            # disable tp for shared experts when enable deepep moe, or with fp4 allgather
+            self.shared_experts = Glm4MoeLiteMLP(
+                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 get_moe_a2a_backend().is_deepep()
+                    or get_moe_a2a_backend().is_mooncake()
+                    or should_use_flashinfer_cutlass_moe_fp4_allgather()
+                    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 get_moe_a2a_backend().is_deepep() or get_moe_a2a_backend().is_mooncake():
+            # TODO: we will support tp < ep in the future
+            self.ep_size = get_moe_expert_parallel_world_size()
+            self.num_experts = (
+                config.n_routed_experts
+                + get_global_server_args().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._enable_a2a_moe = (
+            get_moe_a2a_backend().is_deepep() or get_moe_a2a_backend().is_mooncake()
+        )
+        self._fuse_shared_experts_inside_sbo = SboFlags.fuse_shared_experts_inside_sbo()
+
+
+class Glm4MoeLiteDecoderLayer(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
+
+        from sglang.srt.layers.attention.nsa.utils import is_nsa_enable_prefill_cp
+
+        self.nsa_enable_prefill_cp = is_nsa_enable_prefill_cp()
+        rope_theta = 1000000
+        rope_scaling = None
+        max_position_embeddings = getattr(config, "max_position_embeddings", 202752)
+        self.layer_id = layer_id
+
+        self.self_attn = DeepseekV2AttentionMLA(
+            config=config,
+            hidden_size=config.hidden_size,
+            num_heads=config.num_attention_heads,
+            qk_nope_head_dim=config.qk_nope_head_dim,
+            qk_rope_head_dim=config.qk_rope_head_dim,
+            v_head_dim=config.v_head_dim,
+            q_lora_rank=config.q_lora_rank,
+            kv_lora_rank=config.kv_lora_rank,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
+            reduce_results=False,
+            layer_id=layer_id,
+            prefix=add_prefix("self_attn", prefix),
+        )
+
+        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)
+        is_next_layer_sparse = self._is_layer_sparse(layer_id + 1, is_nextn=False)
+
+        self.layer_scatter_modes = LayerScatterModes.init_new(
+            layer_id=layer_id,
+            num_layers=1 if is_nextn else config.num_hidden_layers,
+            is_layer_sparse=self.is_layer_sparse,
+            is_previous_layer_sparse=is_previous_layer_sparse,
+            is_next_layer_sparse=is_next_layer_sparse,
+        )
+
+        if self.is_layer_sparse:
+            self.mlp = Glm4MoeLiteSparseMoeBlock(
+                config=config,
+                quant_config=quant_config,
+                prefix=add_prefix("mlp", prefix),
+                layer_id=self.layer_id,
+                alt_stream=alt_stream,
+                is_nextn=is_nextn,
+            )
+        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 = Glm4MoeLiteMLP(
+                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,
+            allow_reduce_scatter=True,
+            is_last_layer=(
+                is_nextn or (self.layer_id == self.config.num_hidden_layers - 1)
+            ),
+            qkv_latent_func=self.self_attn.prepare_qkv_latent,
+        )
+
+
+class Glm4MoeLiteModel(DeepseekV2Model):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        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.pp_group = get_pp_group()
+
+        # DeepseekV2Model.forward expects these attributes to exist.
+        from sglang.srt.layers.attention.nsa.utils import is_nsa_enable_prefill_cp
+
+        self.nsa_enable_prefill_cp = is_nsa_enable_prefill_cp()
+        self.cp_size = get_attention_tp_size() if self.nsa_enable_prefill_cp else None
+        self.gemm_output_zero_allocator_size = 0
+        self.llama_4_scaling_config = getattr(config, "llama_4_scaling", None)
+
+        if self.pp_group.is_first_rank:
+            self.embed_tokens = VocabParallelEmbedding(
+                config.vocab_size,
+                config.hidden_size,
+                enable_tp=not is_dp_attention_enabled(),
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        self.alt_stream = torch.cuda.Stream() if _is_cuda else None
+        self.layers, self.start_layer, self.end_layer = make_layers(
+            config.num_hidden_layers,
+            lambda idx, prefix: Glm4MoeLiteDecoderLayer(
+                config=config,
+                layer_id=idx,
+                quant_config=quant_config,
+                prefix=prefix,
+                alt_stream=self.alt_stream,
+            ),
+            pp_rank=self.pp_group.rank_in_group,
+            pp_size=self.pp_group.world_size,
+            prefix=add_prefix("layers", prefix),
+        )
+        if self.pp_group.is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer(return_tuple=True)
+        self.layers_to_capture = []
+
+
+class Glm4MoeLiteForCausalLM(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.pp_group = get_pp_group()
+        self.determine_num_fused_shared_experts("Glm4MoeLiteForCausalLM")
+        self.model = Glm4MoeLiteModel(
+            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=get_global_server_args().enable_dp_lm_head,
+        )
+        self.logits_processor = LogitsProcessor(config)
+
+        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, Glm4MoeLiteSparseMoeBlock)
+            }
+        )
+        self.capture_aux_hidden_states = False
+
+        from sglang.srt.layers.attention.nsa.utils import is_nsa_enable_prefill_cp
+
+        self.nsa_enable_prefill_cp = is_nsa_enable_prefill_cp()
+        if self.nsa_enable_prefill_cp:
+            from sglang.srt.layers.dp_attention import (
+                get_attention_tp_rank,
+                get_attention_tp_size,
+            )
+
+            self.cp_rank = get_attention_tp_rank()
+            self.cp_size = get_attention_tp_size()
+        else:
+            self.cp_rank = self.cp_size = None
+
+    def determine_num_fused_shared_experts(
+        self, architecture: str = "Glm4MoeLiteForCausalLM"
+    ):
+        self.num_fused_shared_experts = 0
+        if get_global_server_args().disable_shared_experts_fusion:
+            return
+
+        disable_reason = None
+        if (
+            not _is_cuda
+            or torch.cuda.get_device_capability("cuda") < (8, 0)
+            or self.config.architectures[0] != architecture
+            or self.config.n_shared_experts != 1
+        ):
+            disable_reason = "Only GLM-4.5 or GLM-4.6 on NV-platform with capability >= 80 can use shared experts fusion optimization."
+        elif get_moe_expert_parallel_world_size() > 1:
+            disable_reason = "GLM-4.5 or GLM-4.6 can not use shared experts fusion optimization under expert parallelism."
+
+        if disable_reason is not None:
+            get_global_server_args().disable_shared_experts_fusion = True
+            self.num_fused_shared_experts = 0
+            log_info_on_rank0(
+                logger,
+                f"{disable_reason} Shared experts fusion optimization is disabled.",
+            )
+            return
+
+        self.num_fused_shared_experts = self.config.n_shared_experts
+
+    def load_weights(
+        self,
+        weights: Iterable[Tuple[str, torch.Tensor]],
+        is_nextn=False,
+        params_dict=None,
+        is_eagle=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
+
+            def iter_weights_with_fused_shared_experts(
+                weights: Iterable[Tuple[str, torch.Tensor]],
+            ) -> Iterable[Tuple[str, torch.Tensor]]:
+                import re
+
+                pattern = re.compile(
+                    r"^model\.layers\.(\d+)\.mlp\.shared_experts\.(.+)$"
+                )
+                for name, weight in weights:
+                    match = pattern.match(name)
+                    if match:
+                        layer_id = int(match.group(1))
+                        suffix = match.group(2)
+                        name = f"model.layers.{layer_id}.mlp.experts.{self.config.n_routed_experts}.{suffix}"
+                    yield name, weight
+
+            weights = iter_weights_with_fused_shared_experts(weights)
+
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        expert_params_mapping = FusedMoE.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",
+            ]
+        else:
+            nextn_layer_prefix = None
+            nextn_spec_weight_names = []
+
+        eagle_ignore_weight_names = []
+        if is_eagle:
+            eagle_ignore_weight_names = [
+                "eagle_draft_tokens_map",
+                "eagle_lm_head.weight",
+            ]
+
+        if params_dict is None:
+            params_dict = dict(self.named_parameters())
+
+        weight_names = []
+        for name, loaded_weight in weights:
+            weight_names.append(name)
+
+            if not is_nextn and not is_eagle:
+                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 nextn_layer_prefix and not name.startswith(nextn_layer_prefix):
+                    continue
+
+                if nextn_layer_prefix is not None:  # mtp
+                    # 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:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Track if this is an expert weight to enable early skipping
+                is_expert_weight = False
+
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+
+                    # Mark as expert weight regardless of whether we can process it
+                    is_expert_weight = True
+
+                    name = name.replace(weight_name, param_name)
+                    if name not in params_dict:
+                        # Expert weight not on this rank, will be skipped below
+                        continue
+
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(
+                        param,
+                        loaded_weight,
+                        name,
+                        shard_id=shard_id,
+                        expert_id=expert_id,
+                    )
+                    break
+                else:
+                    if is_expert_weight:
+                        # This is an expert weight but not mapped to this rank, skip all remaining processing
+                        continue
+
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    if name in eagle_ignore_weight_names:
+                        continue
+
+                    # GLM NOTE: for MLA
+                    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"
+                                )
+                            )
+                            if param_name not in params_dict:
+                                continue
+                            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}"
+                                )
+
+                    if name not in params_dict:
+                        continue
+
+                    if name in params_dict.keys():
+                        param = params_dict[name]
+                        weight_loader = getattr(
+                            param, "weight_loader", default_weight_loader
+                        )
+                        weight_loader(param, loaded_weight)
+                    else:
+                        logger.warning(f"Parameter {name} not found in params_dict")
+
+        # DeepseekV2AttentionMLA.forward_* expects post_load_weights() to populate
+        # per-layer packed weights like `w_kc`/`w_vc` (used during CUDA graph capture).
+        # GLM-Lite configs may not set `config.mla`, but this model always uses
+        # DeepseekV2AttentionMLA, so we must run the post-load processing.
+        # Use weight_names=None to ensure we always process all layers. Some checkpoints /
+        # naming schemes may not include "kv_b_proj" in `weight_names`, but `w_kc`/`w_vc`
+        # are still required by DeepseekV2AttentionMLA at runtime.
+        self.post_load_weights(is_nextn=is_nextn, weight_names=None)
+
+
+EntryClass = [Glm4MoeLiteForCausalLM]

python/sglang/srt/server_args.py

@@ -1586,6 +1586,7 @@ class ServerArgs:
                 "DeepseekV3ForCausalLM",
                 "GptOssForCausalLM",
                 "Glm4MoeForCausalLM",
+                "Glm4MoeLiteForCausalLM",
                 "Qwen3MoeForCausalLM",
             ]
             and (is_sm90_supported() or is_sm100_supported())
@@ -2147,6 +2148,7 @@ class ServerArgs:
                 "DeepseekV32ForCausalLM",
                 "DeepseekV3ForCausalLM",
                 "Glm4MoeForCausalLM",
+                "Glm4MoeLiteForCausalLM",
                 "BailingMoeForCausalLM",
                 "BailingMoeV2ForCausalLM",
                 "MistralLarge3ForCausalLM",
@@ -5422,6 +5424,7 @@ def auto_choose_speculative_params(self: ServerArgs):
         "DeepseekV2ForCausalLM",
         "GptOssForCausalLM",
         "Glm4MoeForCausalLM",
+        "Glm4MoeLiteForCausalLM",
         "BailingMoeForCausalLM",
         "BailingMoeV2ForCausalLM",
         "MistralLarge3ForCausalLM",