# 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, use_attn_tp_group=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 cannot 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]