Refactoring GLM-4.5 and GLM-4.5V related implementations (#11800)

python/sglang/srt/models/glm4_moe.py

@@ -15,7 +15,7 @@
 """Inference-only GLM-4.5, GLM-4.6 model compatible with HuggingFace weights"""
 
 import logging
-from typing import Any, Dict, Iterable, Optional, Tuple
+from typing import Any, Dict, Iterable, Optional, Tuple, Union
 
 import torch
 import torch.nn.functional as F
@@ -27,10 +27,16 @@ from sglang.srt.distributed import (
     get_pp_group,
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
+    parallel_state,
     tensor_model_parallel_all_reduce,
 )
+from sglang.srt.distributed.device_communicators.pynccl_allocator import (
+    use_symmetric_memory,
+)
+from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
+from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation
+from sglang.srt.eplb.expert_location_dispatch import ExpertLocationDispatchInfo
 from sglang.srt.layers.activation import SiluAndMul
-from sglang.srt.layers.amx_utils import PackWeightMethod
 from sglang.srt.layers.communicator import (
     LayerCommunicator,
     LayerScatterModes,
@@ -48,7 +54,10 @@ from sglang.srt.layers.linear import (
     RowParallelLinear,
 )
 from sglang.srt.layers.logits_processor import LogitsProcessor
-from sglang.srt.layers.moe import get_moe_a2a_backend
+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
@@ -56,23 +65,17 @@ from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.quantization.fp8_kernel import is_fp8_fnuz
 from sglang.srt.layers.radix_attention import RadixAttention
 from sglang.srt.layers.rotary_embedding import get_rope
+from sglang.srt.layers.utils import PPMissingLayer
 from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,
 )
 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_executor.forward_batch_info import ForwardBatch, PPProxyTensors
 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.server_args import get_global_server_args
+from sglang.srt.two_batch_overlap import model_forward_maybe_tbo
 from sglang.srt.utils import (
-    BumpAllocator,
-    LazyValue,
     add_prefix,
     cpu_has_amx_support,
     get_bool_env_var,
@@ -80,8 +83,7 @@ from sglang.srt.utils import (
     is_cpu,
     is_cuda,
     is_hip,
-    log_info_on_rank0,
+    make_layers,
-    use_intel_amx_backend,
 )
 
 _is_hip = is_hip()
@@ -92,11 +94,6 @@ _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__)
 
 
@@ -136,8 +133,7 @@ class Glm4MoeMLP(nn.Module):
         )
         if hidden_act != "silu":
             raise ValueError(
-                f"Unsupported activation: {hidden_act}. "
+                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
-                "Only silu is supported for now."
             )
         self.act_fn = SiluAndMul()
 
@@ -146,7 +142,6 @@ class Glm4MoeMLP(nn.Module):
         x,
         forward_batch=None,
         should_allreduce_fusion=False,
-        gemm_output_zero_allocator: BumpAllocator = None,
     ):
         if (self.tp_size == 1) and x.shape[0] == 0:
             return x
@@ -326,47 +321,21 @@ class Glm4MoeGate(nn.Module):
         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)
         )
-        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):
+class Glm4MoeSparseMoeBlock(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
@@ -374,18 +343,12 @@ class Glm4MoeSparseMoeBlock(DeepseekV2MoE):
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
         alt_stream: Optional[torch.cuda.Stream] = None,
-        is_nextn: bool = False,
     ):
         nn.Module.__init__(self)
+        self.top_k = config.num_experts_per_tok
         self.tp_size = get_tensor_model_parallel_world_size()
-        self.ep_size = get_moe_expert_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
@@ -402,39 +365,31 @@ class Glm4MoeSparseMoeBlock(DeepseekV2MoE):
                 "Only silu is supported for now."
             )
 
-        self.gate = Glm4MoeGate(
+        self.gate = Glm4MoeGate(config=config, prefix=add_prefix("gate", prefix))
-            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,
+            top_k=self.top_k,
             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,
         )
 
         self.experts = get_moe_impl_class(quant_config)(
-            num_experts=config.n_routed_experts
+            num_experts=config.n_routed_experts,
-            + self.num_fused_shared_experts
+            top_k=self.top_k,
-            + get_global_server_args().ep_num_redundant_experts,
+            layer_id=self.layer_id,
-            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.shared_experts_is_int8 = False
+        # shared expert
-        self.shared_experts_is_fp8 = False
+        if config.n_shared_experts is not None:
-        # 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,
@@ -443,21 +398,14 @@ class Glm4MoeSparseMoeBlock(DeepseekV2MoE):
                 quant_config=quant_config,
                 reduce_results=False,
                 prefix=add_prefix("shared_experts", prefix),
-                **(dict(tp_rank=0, tp_size=1) if self.ep_size > 1 else {}),
+                **(
+                    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
@@ -479,12 +427,46 @@ class Glm4MoeSparseMoeBlock(DeepseekV2MoE):
             get_moe_a2a_backend().is_deepep() or get_moe_a2a_backend().is_mooncake()
         )
 
+    def get_moe_weights(self):
+        return [
+            x.data
+            for name, x in self.experts.named_parameters()
+            if name not in ["correction_bias"]
+        ]
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        forward_batch: Optional[ForwardBatch] = None,
+        should_allreduce_fusion: bool = False,
+        use_reduce_scatter: bool = False,
+    ) -> torch.Tensor:
+        if not self._enable_a2a_moe:
+            DUAL_STREAM_TOKEN_THRESHOLD = 1024
+            if (
+                self.alt_stream is not None
+                and hidden_states.shape[0] > 0
+                and hidden_states.shape[0] <= DUAL_STREAM_TOKEN_THRESHOLD
+            ):
+                return self.forward_normal_dual_stream(
+                    hidden_states,
+                    should_allreduce_fusion,
+                    use_reduce_scatter,
+                )
+            else:
+                return self.forward_normal(
+                    hidden_states,
+                    should_allreduce_fusion,
+                    use_reduce_scatter,
+                )
+        else:
+            return self.forward_deepep(hidden_states, forward_batch)
+
     def forward_normal_dual_stream(
         self,
         hidden_states: torch.Tensor,
         should_allreduce_fusion: bool = False,
         use_reduce_scatter: bool = False,
-        gemm_output_zero_allocator: BumpAllocator = None,
     ) -> torch.Tensor:
 
         current_stream = torch.cuda.current_stream()
@@ -498,28 +480,21 @@ class Glm4MoeSparseMoeBlock(DeepseekV2MoE):
             final_hidden_states = self.experts(hidden_states, topk_output)
             if not _is_cuda:
                 final_hidden_states *= self.routed_scaling_factor
-        current_stream.wait_stream(self.alt_stream)
 
-        if self.ep_size > 1:
+        current_stream.wait_stream(self.alt_stream)
+        with use_symmetric_memory(parallel_state.get_tp_group()) as sm:
+            final_hidden_states_out = torch.empty_like(final_hidden_states)
+
+        torch.add(final_hidden_states, shared_output, out=final_hidden_states_out)
+        final_hidden_states = final_hidden_states_out
+        sm.tag(final_hidden_states)
         if (
             self.tp_size > 1
             and not should_allreduce_fusion
             and not use_reduce_scatter
+            and not should_use_flashinfer_cutlass_moe_fp4_allgather()
         ):
-                final_hidden_states = tensor_model_parallel_all_reduce(
+            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
-                    final_hidden_states
-                )
-            final_hidden_states += shared_output
-        else:
-            final_hidden_states += shared_output
-            if (
-                self.tp_size > 1
-                and not should_allreduce_fusion
-                and not use_reduce_scatter
-            ):
-                final_hidden_states = tensor_model_parallel_all_reduce(
-                    final_hidden_states
-                )
         return final_hidden_states
 
     def forward_normal(
@@ -527,39 +502,69 @@ class Glm4MoeSparseMoeBlock(DeepseekV2MoE):
         hidden_states: torch.Tensor,
         should_allreduce_fusion: bool = False,
         use_reduce_scatter: bool = False,
-        gemm_output_zero_allocator: BumpAllocator = None,
     ) -> torch.Tensor:
-        if hasattr(self, "shared_experts") and use_intel_amx_backend(
+        if hidden_states.shape[0] > 0:
-            self.shared_experts.gate_up_proj
-        ):
-            return self.forward_cpu(hidden_states, should_allreduce_fusion)
-
             shared_output = self._forward_shared_experts(hidden_states)
             # router_logits: (num_tokens, n_experts)
             router_logits = self.gate(hidden_states)
             topk_output = self.topk(hidden_states, router_logits)
+        else:
+            shared_output = None
+            topk_output = self.topk.empty_topk_output(hidden_states.device)
+
         final_hidden_states = self.experts(hidden_states, topk_output)
         if not _is_cuda and not _use_aiter:
             # fused in biased_grouped_topk so we can skip here
             final_hidden_states *= self.routed_scaling_factor
-        if self.ep_size > 1:
-            if self.tp_size > 1 and not should_allreduce_fusion:
-                final_hidden_states = tensor_model_parallel_all_reduce(
-                    final_hidden_states
-                )
         if shared_output is not None:
-                final_hidden_states += shared_output
+            with use_symmetric_memory(parallel_state.get_tp_group()) as sm:
-        else:
+                final_hidden_states_out = torch.empty_like(final_hidden_states)
-            if shared_output is not None:
+            torch.add(final_hidden_states, shared_output, out=final_hidden_states_out)
-                final_hidden_states += shared_output
+            final_hidden_states = final_hidden_states_out
-            if self.tp_size > 1 and not should_allreduce_fusion:
+            sm.tag(final_hidden_states)
-                final_hidden_states = tensor_model_parallel_all_reduce(
+        if (
-                    final_hidden_states
+            self.tp_size > 1
-                )
+            and not should_allreduce_fusion
+            and not use_reduce_scatter
+            and not should_use_flashinfer_cutlass_moe_fp4_allgather()
+        ):
+            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
         return final_hidden_states
 
+    def _forward_deepep(self, hidden_states: torch.Tensor, forward_batch: ForwardBatch):
+        shared_output = None
+        if hidden_states.shape[0] > 0:
+            # router_logits: (num_tokens, n_experts)
+            router_logits, _ = self.gate(hidden_states)
+            shared_output = self._forward_shared_experts(hidden_states)
+            topk_output = self.topk(
+                hidden_states,
+                router_logits,
+                num_token_non_padded=forward_batch.num_token_non_padded,
+                expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
+                    layer_id=self.layer_id,
+                ),
+            )
+        else:
+            topk_output = self.topk.empty_topk_output(hidden_states.device)
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states,
+            topk_output=topk_output,
+        )
 
-class Glm4MoeDecoderLayer(DeepseekV2DecoderLayer):
+        if shared_output is not None:
+            final_hidden_states.add_(shared_output)
+
+        return final_hidden_states
+
+    def _forward_shared_experts(self, hidden_states: torch.Tensor):
+        shared_output = None
+        if hidden_states.shape[0] > 0:
+            shared_output = self.shared_experts(hidden_states)
+        return shared_output
+
+
+class Glm4MoeDecoderLayer(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
@@ -582,6 +587,7 @@ class Glm4MoeDecoderLayer(DeepseekV2DecoderLayer):
         rms_norm_eps = config.rms_norm_eps
         attention_bias = config.attention_bias
         self.layer_id = layer_id
+
         self.self_attn = Glm4MoeAttention(
             hidden_size=self.hidden_size,
             num_heads=config.num_attention_heads,
@@ -597,15 +603,15 @@ class Glm4MoeDecoderLayer(DeepseekV2DecoderLayer):
             quant_config=quant_config,
             prefix=add_prefix("self_attn", prefix),
             use_qk_norm=config.use_qk_norm,
+            alt_stream=alt_stream,
         )
 
         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,
+            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,
         )
@@ -616,6 +622,7 @@ class Glm4MoeDecoderLayer(DeepseekV2DecoderLayer):
                 quant_config=quant_config,
                 prefix=add_prefix("mlp", prefix),
                 layer_id=self.layer_id,
+                alt_stream=alt_stream,
             )
         else:
             if enable_moe_dense_fully_dp():
@@ -641,7 +648,16 @@ class Glm4MoeDecoderLayer(DeepseekV2DecoderLayer):
             layer_scatter_modes=self.layer_scatter_modes,
             input_layernorm=self.input_layernorm,
             post_attention_layernorm=self.post_attention_layernorm,
-            allow_reduce_scatter=False,
+            allow_reduce_scatter=True,
+            is_last_layer=(
+                is_nextn or (self.layer_id == self.config.num_hidden_layers - 1)
+            ),
+        )
+
+    def _is_layer_sparse(self, layer_id: int, is_nextn: bool) -> bool:
+        return is_nextn or (
+            self.config.n_routed_experts is not None
+            and layer_id >= self.config.first_k_dense_replace
         )
 
     def forward(
@@ -650,8 +666,6 @@ class Glm4MoeDecoderLayer(DeepseekV2DecoderLayer):
         hidden_states: torch.Tensor,
         forward_batch: ForwardBatch,
         residual: Optional[torch.Tensor],
-        zero_allocator: BumpAllocator,
-        gemm_output_zero_allocator: BumpAllocator = None,
     ) -> torch.Tensor:
         hidden_states, residual = self.layer_communicator.prepare_attn(
             hidden_states, residual, forward_batch
@@ -676,44 +690,119 @@ class Glm4MoeDecoderLayer(DeepseekV2DecoderLayer):
         return hidden_states, residual
 
 
-class Glm4MoeModel(DeepseekV2Model):
+class Glm4MoeModel(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
-    ) -> None:
+    ):
-        nn.Module.__init__(self)
+        super().__init__()
-        self.padding_id = config.pad_token_id
+        self.pp_group = get_pp_group()
+        self.config = config
         self.vocab_size = config.vocab_size
-        self.first_k_dense_replace = config.first_k_dense_replace
+        self.embed_dim = config.hidden_size
-
+        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 = nn.ModuleList(
+        self.layers, self.start_layer, self.end_layer = make_layers(
-            [
+            config.num_hidden_layers,
-                Glm4MoeDecoderLayer(
+            lambda idx, prefix: Glm4MoeDecoderLayer(
-                    config,
+                layer_id=idx,
-                    layer_id,
+                config=config,
                 quant_config=quant_config,
-                    prefix=add_prefix(f"layers.{layer_id}", prefix),
+                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),
         )
-                for layer_id in range(config.num_hidden_layers)
+        if self.pp_group.is_last_rank:
-            ]
+            self.norm = RMSNorm(self.embed_dim, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer(return_tuple=True)
+
+    def get_input_embeddings(self) -> torch.Tensor:
+        return self.embed_tokens
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+        pp_proxy_tensors: Optional[PPProxyTensors] = None,
+    ) -> Union[torch.Tensor, PPProxyTensors]:
+        if self.pp_group.is_first_rank:
+            if input_embeds is None:
+                hidden_states = self.embed_tokens(input_ids)
+            else:
+                hidden_states = input_embeds
+            residual = None
+        else:
+            assert pp_proxy_tensors is not None
+            hidden_states = pp_proxy_tensors["hidden_states"]
+            residual = pp_proxy_tensors["residual"]
+
+        normal_start_layer = self.start_layer
+        normal_end_layer = self.end_layer
+        if forward_batch.can_run_tbo:
+            if (
+                self.first_k_dense_replace > normal_start_layer
+                and self.first_k_dense_replace < normal_end_layer
+            ):
+                normal_end_layer = self.first_k_dense_replace
+            elif self.first_k_dense_replace < normal_start_layer:
+                normal_end_layer = normal_start_layer = 0
+
+        for i in range(normal_start_layer, normal_end_layer):
+            with get_global_expert_distribution_recorder().with_current_layer(i):
+                layer = self.layers[i]
+                hidden_states, residual = layer(
+                    positions,
+                    hidden_states,
+                    forward_batch,
+                    residual,
                 )
-        self.pp_group = get_pp_group()
+
-        self.start_layer = 0
+        if normal_end_layer != self.end_layer:
-        self.end_layer = config.num_hidden_layers
+            hidden_states, residual = model_forward_maybe_tbo(
-        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+                layers=self.layers[normal_end_layer : self.end_layer],
+                enable_tbo=True,
+                positions=positions,
+                forward_batch=forward_batch,
+                hidden_states=hidden_states,
+                residual=residual,
+                input_data_scatter_mode=self.layers[
+                    normal_end_layer - 1
+                ].layer_scatter_modes.layer_output_mode,
+            )
+
+        if not self.pp_group.is_last_rank:
+            return PPProxyTensors(
+                {
+                    "hidden_states": hidden_states,
+                    "residual": residual,
+                }
+            )
+        else:
+            if not forward_batch.forward_mode.is_idle():
+                if residual is None:
+                    hidden_states = self.norm(hidden_states)
+                else:
+                    hidden_states, _ = self.norm(hidden_states, residual)
+            return hidden_states
 
 
-class Glm4MoeForCausalLM(DeepseekV2ForCausalLM):
+class Glm4MoeForCausalLM(nn.Module):
-
     def __init__(
         self,
         config: PretrainedConfig,
@@ -721,12 +810,10 @@ class Glm4MoeForCausalLM(DeepseekV2ForCausalLM):
         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("Glm4MoeForCausalLM")
         self.model = Glm4MoeModel(
             config, quant_config, prefix=add_prefix("model", prefix)
         )
@@ -739,49 +826,41 @@ class Glm4MoeForCausalLM(DeepseekV2ForCausalLM):
         )
         self.logits_processor = LogitsProcessor(config)
 
-        self._routed_experts_weights_of_layer = LazyValue(
+        # For EAGLE3 support
-            lambda: {
+        self.capture_aux_hidden_states = False
-                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 = "Glm4MoeForCausalLM"
-    ):
-        self.num_fused_shared_experts = 0
-        if get_global_server_args().disable_shared_experts_fusion:
-            return
-
-        # Only Deepseek V3/R1 can use shared experts fusion optimization now.
-        disable_reason = None
-        if (
-            not _is_cuda
-            or torch.cuda.get_device_capability("cuda") < (8, 0)
-            or self.config.architectures[0] != architecture
-            or self.config.n_shared_experts != 1
-        ):
-            disable_reason = "Only GLM-4.5 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 = "Deepseek and 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 get_input_embeddings(self) -> nn.Embedding:
         return self.model.embed_tokens
 
-    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]], is_nextn=False):
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+        pp_proxy_tensors: Optional[PPProxyTensors] = None,
+    ) -> torch.Tensor:
+        hidden_states = self.model(
+            input_ids, positions, forward_batch, input_embeds, pp_proxy_tensors
+        )
 
+        if self.pp_group.is_last_rank:
+            return self.logits_processor(
+                input_ids, hidden_states, self.lm_head, forward_batch
+            )
+        else:
+            return hidden_states
+
+    @property
+    def start_layer(self):
+        return self.model.start_layer
+
+    @property
+    def end_layer(self):
+        return self.model.end_layer
+
+    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
@@ -803,117 +882,14 @@ class Glm4MoeForCausalLM(DeepseekV2ForCausalLM):
             ("gate_up_proj", "gate_proj", 0),
             ("gate_up_proj", "up_proj", 1),
         ]
-        if self.num_fused_shared_experts > 0:
-            assert self.num_fused_shared_experts == 1
-            weights_list = list(weights)
-            weights_dict = dict(weights_list)
-            if self.quant_config is not None:
-                if self.quant_config.get_name() == "w8a8_int8":
-                    suffix_list = [
-                        "down_proj.weight",
-                        "down_proj.weight_scale",
-                        "gate_proj.weight",
-                        "gate_proj.weight_scale",
-                        "up_proj.weight",
-                        "up_proj.weight_scale",
-                    ]
-                elif (
-                    self.quant_config.get_name() == "fp8"
-                    or self.quant_config.get_name() == "blockwise_int8"
-                    or self.quant_config.get_name() == "compressed_tensors"
-                ):
-                    suffix_list = [
-                        "down_proj.weight",
-                        "down_proj.weight_scale",
-                        "gate_proj.weight",
-                        "gate_proj.weight_scale",
-                        "up_proj.weight",
-                        "up_proj.weight_scale",
-                    ]
-                elif self.quant_config.get_name() == "awq":
-                    suffix_list = [
-                        "down_proj.qweight",
-                        "down_proj.qzeros",
-                        "down_proj.scales",
-                        "gate_proj.qweight",
-                        "gate_proj.qzeros",
-                        "gate_proj.scales",
-                        "up_proj.qweight",
-                        "up_proj.qzeros",
-                        "up_proj.scales",
-                    ]
-                elif self.quant_config.get_name() == "modelopt_fp4":
-                    suffix_list = [
-                        "down_proj.weight",
-                        "down_proj.weight_scale",
-                        "down_proj.weight_scale_2",
-                        "down_proj.input_scale",
-                        "gate_proj.weight",
-                        "gate_proj.weight_scale",
-                        "gate_proj.weight_scale_2",
-                        "gate_proj.input_scale",
-                        "up_proj.weight",
-                        "up_proj.weight_scale",
-                        "up_proj.weight_scale_2",
-                        "up_proj.input_scale",
-                    ]
-                else:
-                    raise ValueError(
-                        f"Unsupported shared expert fusion for quantization: {self.quant_config.get_name()}."
-                    )
-            else:
-                suffix_list = [
-                    "down_proj.weight",
-                    "gate_proj.weight",
-                    "up_proj.weight",
-                ]
-            names_to_remove = []
 
-            moe_layers = (
-                range(
-                    self.config.first_k_dense_replace,
-                    self.config.num_hidden_layers,
-                    self.config.moe_layer_freq,
-                )
-                if not is_nextn
-                else [nextn_layer_id]
-            )
-
-            for moe_layer in moe_layers:
-                for suffix in suffix_list:
-                    shared_expert_weight_name = (
-                        f"model.layers.{moe_layer}.mlp.shared_experts.{suffix}"
-                    )
-                    # online fp8 quantization does not load weight_scale
-                    if shared_expert_weight_name not in weights_dict:
-                        continue
-                    weights_list.append(
-                        (
-                            f"model.layers.{moe_layer}."
-                            f"mlp.experts."
-                            f"{self.config.n_routed_experts + 0}"
-                            f".{suffix}",
-                            weights_dict[shared_expert_weight_name],
-                        )
-                    )
-                    names_to_remove += [shared_expert_weight_name]
-            weights = [w for w in weights_list if w[0] not in names_to_remove]
-
-        # Params for weights, fp8 weight scales, fp8 activation scales
-        # (param_name, weight_name, expert_id, shard_id)
         expert_params_mapping = 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,
+            num_experts=self.config.n_routed_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 = [
@@ -969,22 +945,36 @@ class Glm4MoeForCausalLM(DeepseekV2ForCausalLM):
                 # 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:
+                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(
@@ -996,65 +986,43 @@ class Glm4MoeForCausalLM(DeepseekV2ForCausalLM):
                     )
                     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 fuse_qkv_a_proj and (
+                    if name not in params_dict:
-                        "q_a_proj" in name or "kv_a_proj_with_mqa" in name
+                        continue
-                    ):
-                        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 name in params_dict.keys():
-                        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)
+                    else:
+                        logger.warning(f"Parameter {name} not found in params_dict")
+
+    def get_embed_and_head(self):
+        return self.model.embed_tokens.weight, self.lm_head.weight
+
+    def set_embed_and_head(self, embed, head):
+        del self.model.embed_tokens.weight
+        del self.lm_head.weight
+        self.model.embed_tokens.weight = embed
+        self.lm_head.weight = head
+        torch.cuda.empty_cache()
+        torch.cuda.synchronize()
+
+    @classmethod
+    def get_model_config_for_expert_location(cls, config):
+        return ModelConfigForExpertLocation(
+            num_layers=config.num_hidden_layers,
+            num_logical_experts=config.n_routed_experts,
+            num_groups=config.n_group,
+        )
 
 
 EntryClass = [Glm4MoeForCausalLM]

python/sglang/srt/models/glm4_moe_nextn.py

@@ -12,7 +12,8 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Inference-only GLM-4.5, GLM-4.6 NextN Speculative Decoding."""
+"""Inference-only GLM-4.5, GLM-4.6 Speculative Decoding."""
+
 import logging
 from typing import Iterable, Optional, Tuple
 
@@ -33,7 +34,7 @@ from sglang.srt.layers.vocab_parallel_embedding import (
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.models.glm4_moe import Glm4MoeDecoderLayer, Glm4MoeForCausalLM
 from sglang.srt.server_args import get_global_server_args
-from sglang.srt.utils import BumpAllocator, add_prefix
+from sglang.srt.utils import add_prefix
 
 logger = logging.getLogger(__name__)
 
@@ -84,14 +85,6 @@ class Glm4MoeModelNextN(nn.Module):
         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:
@@ -111,7 +104,7 @@ class Glm4MoeModelNextN(nn.Module):
         residual = None
         with get_global_expert_distribution_recorder().disable_this_region():
             hidden_states, residual = self.decoder(
-                positions, hidden_states, forward_batch, residual, zero_allocator
+                positions, hidden_states, forward_batch, residual
             )
 
         if not forward_batch.forward_mode.is_idle():
@@ -124,7 +117,6 @@ class Glm4MoeModelNextN(nn.Module):
 
 
 class Glm4MoeForCausalLMNextN(Glm4MoeForCausalLM):
-
     def __init__(
         self,
         config: PretrainedConfig,
@@ -135,8 +127,6 @@ class Glm4MoeForCausalLMNextN(Glm4MoeForCausalLM):
         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)
         )

python/sglang/srt/models/glm4v_moe.py

@@ -6,13 +6,10 @@ import torch
 import torch.nn as nn
 from transformers.models.glm4v_moe.configuration_glm4v_moe import Glm4vMoeConfig
 
-from sglang.srt.distributed import (
+from sglang.srt.distributed import get_tensor_model_parallel_world_size
-    get_moe_expert_parallel_world_size,
-    get_tensor_model_parallel_world_size,
-)
 from sglang.srt.layers.attention import vision_utils
 from sglang.srt.layers.logits_processor import LogitsProcessor
-from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
+from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
 from sglang.srt.layers.pooler import Pooler, PoolingType
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
@@ -20,7 +17,7 @@ from sglang.srt.model_loader.weight_utils import default_weight_loader
 from sglang.srt.models.glm4_moe import Glm4MoeModel
 from sglang.srt.models.glm4v import Glm4vForConditionalGeneration, Glm4vVisionModel
 from sglang.srt.server_args import get_global_server_args
-from sglang.srt.utils import add_prefix, is_cuda, log_info_on_rank0
+from sglang.srt.utils import add_prefix, is_cuda
 from sglang.srt.utils.hf_transformers_utils import get_processor
 
 _is_cuda = is_cuda()
@@ -39,12 +36,10 @@ class Glm4vMoeForConditionalGeneration(Glm4vForConditionalGeneration):
     ) -> None:
         nn.Module.__init__(self)
 
-        config.moe_layer_freq = 1
         self.config = config
         vision_utils.update_vit_attn_dummy_heads_config(self.config)
         self.tp_size = get_tensor_model_parallel_world_size()
         self.quant_config = quant_config
-        self.determine_num_fused_shared_experts("Glm4MoeForCausalLM")
         self.num_fused_shared_experts = (
             0
             if get_global_server_args().disable_shared_experts_fusion
@@ -77,38 +72,7 @@ class Glm4vMoeForConditionalGeneration(Glm4vForConditionalGeneration):
         # For EAGLE3 support
         self.capture_aux_hidden_states = False
 
-    def determine_num_fused_shared_experts(
-        self, architecture: str = "Glm4MoeForCausalLM"
-    ):
-        self.num_fused_shared_experts = 0
-        if get_global_server_args().disable_shared_experts_fusion:
-            return
-
-        # Only Deepseek V3/R1 can use shared experts fusion optimization now.
-        disable_reason = None
-        if (
-            not _is_cuda
-            or torch.cuda.get_device_capability("cuda") < (8, 0)
-            or self.config.architectures[0] != architecture
-            or self.config.n_shared_experts != 1
-        ):
-            disable_reason = "Only GLM-4.5 on NV-platform with capability >= 80 can use shared experts fusion optimization."
-        elif get_moe_expert_parallel_world_size() > 1:
-            disable_reason = "Deepseek and GLM-4.5 can not use shared experts fusion optimization under expert parallelism."
-
-        if disable_reason is not None:
-            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):
-
         if is_nextn:
             if hasattr(self.config, "num_nextn_predict_layers"):
                 num_nextn_layers = self.config.num_nextn_predict_layers
@@ -130,117 +94,14 @@ class Glm4vMoeForConditionalGeneration(Glm4vForConditionalGeneration):
             ("gate_up_proj", "gate_proj", 0),
             ("gate_up_proj", "up_proj", 1),
         ]
-        if self.num_fused_shared_experts > 0:
-            assert self.num_fused_shared_experts == 1
-            weights_list = list(weights)
-            weights_dict = dict(weights_list)
-            if self.quant_config is not None:
-                if self.quant_config.get_name() == "w8a8_int8":
-                    suffix_list = [
-                        "down_proj.weight",
-                        "down_proj.weight_scale",
-                        "gate_proj.weight",
-                        "gate_proj.weight_scale",
-                        "up_proj.weight",
-                        "up_proj.weight_scale",
-                    ]
-                elif (
-                    self.quant_config.get_name() == "fp8"
-                    or self.quant_config.get_name() == "blockwise_int8"
-                    or self.quant_config.get_name() == "compressed_tensors"
-                ):
-                    suffix_list = [
-                        "down_proj.weight",
-                        "down_proj.weight_scale",
-                        "gate_proj.weight",
-                        "gate_proj.weight_scale",
-                        "up_proj.weight",
-                        "up_proj.weight_scale",
-                    ]
-                elif self.quant_config.get_name() == "awq":
-                    suffix_list = [
-                        "down_proj.qweight",
-                        "down_proj.qzeros",
-                        "down_proj.scales",
-                        "gate_proj.qweight",
-                        "gate_proj.qzeros",
-                        "gate_proj.scales",
-                        "up_proj.qweight",
-                        "up_proj.qzeros",
-                        "up_proj.scales",
-                    ]
-                elif self.quant_config.get_name() == "modelopt_fp4":
-                    suffix_list = [
-                        "down_proj.weight",
-                        "down_proj.weight_scale",
-                        "down_proj.weight_scale_2",
-                        "down_proj.input_scale",
-                        "gate_proj.weight",
-                        "gate_proj.weight_scale",
-                        "gate_proj.weight_scale_2",
-                        "gate_proj.input_scale",
-                        "up_proj.weight",
-                        "up_proj.weight_scale",
-                        "up_proj.weight_scale_2",
-                        "up_proj.input_scale",
-                    ]
-                else:
-                    raise ValueError(
-                        f"Unsupported shared expert fusion for quantization: {self.quant_config.get_name()}."
-                    )
-            else:
-                suffix_list = [
-                    "down_proj.weight",
-                    "gate_proj.weight",
-                    "up_proj.weight",
-                ]
-            names_to_remove = []
 
-            moe_layers = (
-                range(
-                    self.config.first_k_dense_replace,
-                    self.config.num_hidden_layers,
-                    self.config.moe_layer_freq,
-                )
-                if not is_nextn
-                else [nextn_layer_id]
-            )
-
-            for moe_layer in moe_layers:
-                for suffix in suffix_list:
-                    shared_expert_weight_name = (
-                        f"model.layers.{moe_layer}.mlp.shared_experts.{suffix}"
-                    )
-                    # online fp8 quantization does not load weight_scale
-                    if shared_expert_weight_name not in weights_dict:
-                        continue
-                    weights_list.append(
-                        (
-                            f"model.layers.{moe_layer}."
-                            f"mlp.experts."
-                            f"{self.config.n_routed_experts + 0}"
-                            f".{suffix}",
-                            weights_dict[shared_expert_weight_name],
-                        )
-                    )
-                    names_to_remove += [shared_expert_weight_name]
-            weights = [w for w in weights_list if w[0] not in names_to_remove]
-
-        # Params for weights, fp8 weight scales, fp8 activation scales
-        # (param_name, weight_name, expert_id, shard_id)
         expert_params_mapping = 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,
+            num_experts=self.config.n_routed_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 = [
@@ -300,23 +161,36 @@ class Glm4vMoeForConditionalGeneration(Glm4vForConditionalGeneration):
                 # 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:
+                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
-                param = params_dict[name]
+                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(
@@ -328,64 +202,21 @@ class Glm4vMoeForConditionalGeneration(Glm4vForConditionalGeneration):
                     )
                     break
                 else:
+                    if is_expert_weight:
+                        # This is an expert weight but not mapped to this rank, skip all remaining processing
+                        continue
+
                     if "visual" in name:
-                        # adapt to VisionAttention
+                        # adapt to VisionAttention for GLM-V
                         name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")
 
                     # Skip loading extra bias for GPTQ models.
                     if name.endswith(".bias") and name not in params_dict:
                         continue
-                    if fuse_qkv_a_proj and (
+                    if name not in params_dict:
-                        "q_a_proj" in name or "kv_a_proj_with_mqa" in name
+                        continue
-                    ):
-                        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 name in params_dict.keys():
-                        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
@@ -395,6 +226,8 @@ class Glm4vMoeForConditionalGeneration(Glm4vForConditionalGeneration):
                                 self.config, name, loaded_weight
                             )
                         weight_loader(param, loaded_weight)
+                    else:
+                        logger.warning(f"Parameter {name} not found in params_dict")
 
 
 EntryClass = [Glm4vMoeForConditionalGeneration]

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

@@ -17,7 +17,7 @@ class Glm4vImageProcessor(SGLangBaseProcessor):
     def __init__(self, hf_config, server_args, _processor, *args, **kwargs):
         super().__init__(hf_config, server_args, _processor, *args, **kwargs)
 
-        # GLM-4.1V and GLM-4.5V specific tokens
+        # GLM-V specific tokens
         self.IMAGE_TOKEN = "<|image|>"
         self.VIDEO_TOKEN = "<|video|>"
         self.IMAGE_START_TOKEN = "<|begin_of_image|>"