# Copyright 2023-2024 SGLang Team # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # Adapted from # https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/qwen2_moe.py """Inference-only Qwen2MoE model compatible with HuggingFace weights.""" import logging from contextlib import nullcontext from typing import Any, Dict, Iterable, List, Optional, Tuple, Union import torch import torch.nn.functional as F from torch import nn from transformers import PretrainedConfig from sglang.srt.batch_overlap.two_batch_overlap import model_forward_maybe_tbo from sglang.srt.distributed import ( get_moe_expert_parallel_world_size, get_pp_group, get_tensor_model_parallel_world_size, tensor_model_parallel_all_reduce, ) 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.communicator import ( LayerCommunicator, LayerScatterModes, ScatterMode, ) from sglang.srt.layers.dp_attention import ( get_attention_tp_rank, get_attention_tp_size, is_dp_attention_enabled, ) from sglang.srt.layers.layernorm import RMSNorm from sglang.srt.layers.linear import ( MergedColumnParallelLinear, QKVParallelLinear, ReplicatedLinear, RowParallelLinear, ) from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.moe import get_moe_a2a_backend from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class from sglang.srt.layers.moe.fused_moe_triton import FusedMoE from sglang.srt.layers.moe.topk import TopK from sglang.srt.layers.moe.utils import ( RoutingMethodType, filter_moe_weight_param_global_expert, ) from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention from sglang.srt.layers.rotary_embedding import get_rope from sglang.srt.layers.utils import PPMissingLayer, get_layer_id 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, PPProxyTensors from sglang.srt.model_loader.weight_utils import default_weight_loader from sglang.srt.server_args import get_global_server_args from sglang.srt.utils import ( add_prefix, cpu_has_amx_support, is_cpu, is_cuda, make_layers, use_intel_amx_backend, ) logger = logging.getLogger(__name__) _is_cuda = is_cuda() _is_cpu = is_cpu() _is_cpu_amx_available = cpu_has_amx_support() class Qwen2MoeMLP(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.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, should_allreduce_fusion: bool = False, use_reduce_scatter: bool = False, ): 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 Qwen2MoeSparseMoeBlock(nn.Module): def __init__( self, layer_id: int, config: PretrainedConfig, quant_config: Optional[QuantizationConfig] = None, alt_stream: Optional[torch.cuda.Stream] = None, prefix: str = "", ): super().__init__() self.tp_size = get_tensor_model_parallel_world_size() self.layer_id = layer_id self.alt_stream = alt_stream if self.tp_size > config.num_experts: raise ValueError( f"Tensor parallel size {self.tp_size} is greater than " f"the number of experts {config.num_experts}." ) self.topk = TopK( top_k=config.num_experts_per_tok, renormalize=config.norm_topk_prob, layer_id=layer_id, ) self.experts = get_moe_impl_class(quant_config)( layer_id=self.layer_id, top_k=config.num_experts_per_tok, num_experts=config.num_experts + get_global_server_args().ep_num_redundant_experts, hidden_size=config.hidden_size, intermediate_size=config.moe_intermediate_size, quant_config=quant_config, prefix=add_prefix("experts", prefix), routing_method_type=RoutingMethodType.RenormalizeNaive, ) self.gate = ReplicatedLinear( config.hidden_size, config.num_experts, bias=False, quant_config=None, prefix=add_prefix("gate", prefix), ) if config.shared_expert_intermediate_size > 0: self.shared_expert = Qwen2MoeMLP( hidden_size=config.hidden_size, intermediate_size=config.shared_expert_intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, reduce_results=False, prefix=add_prefix("shared_expert", prefix), **( dict(tp_rank=0, tp_size=1) if get_moe_a2a_backend().is_deepep() else {} ), ) else: self.shared_expert = None if _is_cpu and _is_cpu_amx_available: self.shared_expert_gate = ReplicatedLinear( config.hidden_size, 1, bias=False, quant_config=None, prefix=add_prefix("shared_expert_gate", prefix), ) else: self.shared_expert_gate = torch.nn.Linear(config.hidden_size, 1, bias=False) if get_moe_a2a_backend().is_deepep(): # TODO: we will support tp < ep in the future self.ep_size = get_moe_expert_parallel_world_size() self.num_experts = ( config.num_experts + get_global_server_args().ep_num_redundant_experts ) self.top_k = config.num_experts_per_tok def get_moe_weights(self): return [ x.data for name, x in self.experts.named_parameters() if name not in ["correction_bias"] and filter_moe_weight_param_global_expert( name, x, self.experts.num_local_experts ) ] def _forward_shared_experts(self, hidden_states: torch.Tensor): shared_output = None if self.shared_expert is not None: shared_output = self.shared_expert(hidden_states) if self.shared_expert_gate is not None: if use_intel_amx_backend(self.shared_expert_gate): shared_output = torch.ops.sgl_kernel.fused_linear_sigmoid_mul( hidden_states, self.shared_expert_gate.weight, self.shared_expert_gate.bias, True, shared_output, ) else: shared_output = ( F.sigmoid(self.shared_expert_gate(hidden_states)) * shared_output ) return shared_output 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, ) if shared_output is not None: final_hidden_states.add_(shared_output) return final_hidden_states def _forward_router_experts(self, hidden_states: torch.Tensor): # router_logits: (num_tokens, n_experts) router_logits, _ = self.gate(hidden_states) topk_output = self.topk(hidden_states, router_logits) return self.experts(hidden_states, topk_output) def forward_normal_dual_stream( self, hidden_states: torch.Tensor, ) -> torch.Tensor: current_stream = torch.cuda.current_stream() self.alt_stream.wait_stream(current_stream) shared_output = self._forward_shared_experts(hidden_states.clone()) with torch.cuda.stream(self.alt_stream): router_output = self._forward_router_experts(hidden_states) current_stream.wait_stream(self.alt_stream) return router_output, shared_output def forward( self, hidden_states: torch.Tensor, forward_batch: Optional[ForwardBatch] = None, use_reduce_scatter: bool = False, ) -> torch.Tensor: num_tokens, hidden_dim = hidden_states.shape hidden_states = hidden_states.view(-1, hidden_dim) if get_moe_a2a_backend().is_deepep(): return self._forward_deepep(hidden_states, forward_batch) if ( self.alt_stream is not None and hidden_states.shape[0] > 0 and get_is_capture_mode() ): final_hidden_states, shared_output = self.forward_normal_dual_stream( hidden_states ) else: shared_output = self._forward_shared_experts(hidden_states) final_hidden_states = self._forward_router_experts(hidden_states) if shared_output is not None: # In-place add is required to keep final_hidden_states in the # symmetric memory pool (when --enable-symm-mem is used). # An out-of-place add would allocate a new tensor outside symm # memory, breaking subsequent symmetric collective operations. final_hidden_states += shared_output if self.tp_size > 1 and not use_reduce_scatter: final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states) return final_hidden_states.view(num_tokens, hidden_dim) class Qwen2MoeAttention(nn.Module): def __init__( self, hidden_size: int, num_heads: int, num_kv_heads: int, layer_id: int = 0, rope_theta: float = 10000, rope_scaling: Optional[Dict[str, Any]] = None, max_position_embeddings: int = 8192, qkv_bias: int = True, quant_config: Optional[QuantizationConfig] = None, dual_chunk_attention_config: Optional[dict[str, Any]] = None, prefix: str = "", ) -> None: super().__init__() self.hidden_size = hidden_size attn_tp_rank = get_attention_tp_rank() attn_tp_size = get_attention_tp_size() self.total_num_heads = num_heads assert self.total_num_heads % attn_tp_size == 0 self.num_heads = self.total_num_heads // attn_tp_size self.total_num_kv_heads = num_kv_heads if self.total_num_kv_heads >= attn_tp_size: # Number of KV heads is greater than TP size, so we partition # the KV heads across multiple tensor parallel GPUs. assert self.total_num_kv_heads % attn_tp_size == 0 else: # Number of KV heads is less than TP size, so we replicate # the KV heads across multiple tensor parallel GPUs. assert attn_tp_size % self.total_num_kv_heads == 0 self.num_kv_heads = max(1, self.total_num_kv_heads // attn_tp_size) self.head_dim = hidden_size // self.total_num_heads self.q_size = self.num_heads * self.head_dim self.kv_size = self.num_kv_heads * self.head_dim self.scaling = self.head_dim**-0.5 self.rope_theta = rope_theta self.max_position_embeddings = max_position_embeddings self.qkv_proj = QKVParallelLinear( hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=qkv_bias, quant_config=quant_config, tp_rank=attn_tp_rank, tp_size=attn_tp_size, prefix=add_prefix("qkv_proj", prefix), ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, hidden_size, bias=False, quant_config=quant_config, tp_rank=attn_tp_rank, tp_size=attn_tp_size, reduce_results=False, prefix=add_prefix("o_proj", prefix), ) self.rotary_emb = get_rope( self.head_dim, rotary_dim=self.head_dim, max_position=max_position_embeddings, base=rope_theta, rope_scaling=rope_scaling, dual_chunk_attention_config=dual_chunk_attention_config, ) self.attn = RadixAttention( self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_heads, layer_id=layer_id, quant_config=quant_config, prefix=add_prefix("attn", prefix), ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, ) -> torch.Tensor: qkv, _ = self.qkv_proj(hidden_states) q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) q, k = self.rotary_emb(positions, q, k) attn_output = self.attn(q, k, v, forward_batch) output, _ = self.o_proj(attn_output) return output class Qwen2MoeDecoderLayer(nn.Module): def __init__( self, config: PretrainedConfig, layer_id: int, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", alt_stream: Optional[torch.cuda.Stream] = None, ) -> None: super().__init__() self.config = config self.hidden_size = config.hidden_size rope_theta = getattr(config, "rope_theta", 10000) rope_scaling = getattr(config, "rope_scaling", None) max_position_embeddings = getattr(config, "max_position_embeddings", 8192) qkv_bias = getattr(config, "qkv_bias", True) dual_chunk_attention_config = getattr( config, "dual_chunk_attention_config", None ) self.self_attn = Qwen2MoeAttention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=config.num_key_value_heads, layer_id=layer_id, rope_theta=rope_theta, rope_scaling=rope_scaling, max_position_embeddings=max_position_embeddings, quant_config=quant_config, dual_chunk_attention_config=dual_chunk_attention_config, qkv_bias=qkv_bias, prefix=add_prefix("self_attn", prefix), ) self.layer_id = layer_id self.attn_tp_size = get_attention_tp_size() self.attn_tp_rank = get_attention_tp_rank() # Qwen2MoE all layers are sparse and have no nextn now self.is_layer_sparse = True is_previous_layer_sparse = True is_next_layer_sparse = True self.layer_scatter_modes = LayerScatterModes.init_new( layer_id=layer_id, num_layers=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 = Qwen2MoeSparseMoeBlock( layer_id=layer_id, config=config, quant_config=quant_config, alt_stream=alt_stream, prefix=add_prefix("mlp", prefix), ) else: self.mlp = Qwen2MoeMLP( hidden_size=config.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, prefix=add_prefix("mlp", prefix), ) 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, ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, residual: Optional[torch.Tensor], captured_last_layer_outputs: Optional[List[torch.Tensor]] = None, **kwargs, ) -> Tuple[torch.Tensor, torch.Tensor]: hidden_states, residual = ( self.layer_communicator.prepare_attn_and_capture_last_layer_outputs( hidden_states, residual, forward_batch, captured_last_layer_outputs=captured_last_layer_outputs, **kwargs, ) ) if hidden_states.shape[0] != 0: hidden_states = self.self_attn( positions=positions, hidden_states=hidden_states, forward_batch=forward_batch, ) hidden_states, residual = self.layer_communicator.prepare_mlp( hidden_states, residual, forward_batch ) # For DP with padding, reduce scatter can be used instead of all-reduce. use_reduce_scatter = self.layer_communicator.should_use_reduce_scatter( forward_batch ) hidden_states = self.mlp(hidden_states, forward_batch, use_reduce_scatter) hidden_states, residual = self.layer_communicator.postprocess_layer( hidden_states, residual, forward_batch ) return hidden_states, residual class Qwen2MoeModel(nn.Module): def __init__( self, config: PretrainedConfig, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", decoder_layer_type: type[nn.Module] = Qwen2MoeDecoderLayer, alt_stream: Optional[torch.cuda.Stream] = None, ) -> None: super().__init__() self.config = config self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.pp_group = get_pp_group() 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(), prefix=add_prefix("embed_tokens", prefix), ) else: self.embed_tokens = PPMissingLayer() # Use the provided decoder layer type or default to Qwen2MoeDecoderLayer decoder_layer_type = decoder_layer_type or Qwen2MoeDecoderLayer self.layers, self.start_layer, self.end_layer = make_layers( config.num_hidden_layers, lambda idx, prefix: decoder_layer_type( layer_id=idx, config=config, quant_config=quant_config, prefix=prefix, alt_stream=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) # For EAGLE3 support self.layers_to_capture = [] def set_eagle3_layers_to_capture(self, layers_to_capture: List[int]): self.layers_to_capture = layers_to_capture for layer_id in self.layers_to_capture: setattr(self.layers[layer_id], "_is_layer_to_capture", True) 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"] aux_hidden_states = [] if forward_batch.can_run_tbo: hidden_states, residual = model_forward_maybe_tbo( layers=self.layers, enable_tbo=True, input_data_scatter_mode=ScatterMode.model_input_output(), positions=positions, forward_batch=forward_batch, hidden_states=hidden_states, residual=residual, ) else: for i in range(self.start_layer, self.end_layer): ctx = ( nullcontext() if get_global_server_args().enable_piecewise_cuda_graph else get_global_expert_distribution_recorder().with_current_layer(i) ) with ctx: layer = self.layers[i] hidden_states, residual = layer( positions, hidden_states, forward_batch, residual, captured_last_layer_outputs=( aux_hidden_states if getattr(layer, "_is_layer_to_capture", False) else None ), ) if not self.pp_group.is_last_rank: return PPProxyTensors( { "hidden_states": hidden_states, "residual": residual, } ) else: if hidden_states.shape[0] != 0: if residual is None: hidden_states = self.norm(hidden_states) else: hidden_states, _ = self.norm(hidden_states, residual) if len(aux_hidden_states) == 0: return hidden_states return hidden_states, aux_hidden_states class Qwen2MoeForCausalLM(nn.Module): fall_back_to_pt_during_load = False def __init__( self, config: PretrainedConfig, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() self.pp_group = get_pp_group() self.config = config self.quant_config = quant_config alt_stream = torch.cuda.Stream() if _is_cuda else None self.model = Qwen2MoeModel( config, quant_config, prefix=add_prefix("model", prefix), alt_stream=alt_stream, ) 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) # For EAGLE3 support self.capture_aux_hidden_states = 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=pp_proxy_tensors, ) aux_hidden_states = None if self.capture_aux_hidden_states: hidden_states, aux_hidden_states = hidden_states if self.pp_group.is_last_rank: return self.logits_processor( input_ids, hidden_states, self.lm_head, forward_batch, aux_hidden_states ) else: return hidden_states @torch.no_grad() def forward_split_prefill( self, input_ids: torch.Tensor, positions: torch.Tensor, forward_batch: ForwardBatch, split_interval: Tuple[int, int], # [start, end) 0-based input_embeds: torch.Tensor = None, ): start, end = split_interval # embed if start == 0: if input_embeds is None: forward_batch.hidden_states = self.model.embed_tokens(input_ids) else: forward_batch.hidden_states = input_embeds # decoder layer for i in range(start, end): with get_global_expert_distribution_recorder().with_current_layer(i): layer = self.model.layers[i] forward_batch.hidden_states, forward_batch.residual = layer( positions, forward_batch.hidden_states, forward_batch, forward_batch.residual, ) if end == self.model.config.num_hidden_layers: # norm hidden_states, _ = self.model.norm( forward_batch.hidden_states, forward_batch.residual ) forward_batch.hidden_states = hidden_states # logits process result = self.logits_processor( input_ids, forward_batch.hidden_states, self.lm_head, forward_batch ) else: result = None return result @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]]): 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), ] 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.num_experts, ) params_dict = dict(self.named_parameters()) for name, loaded_weight in weights: layer_id = get_layer_id(name) if ( layer_id is not None and hasattr(self.model, "start_layer") and ( layer_id < self.model.start_layer or layer_id >= self.model.end_layer ) ): continue 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: for mapping in expert_params_mapping: param_name, weight_name, expert_id, shard_id = mapping if weight_name not in name: continue name = name.replace(weight_name, param_name) param = params_dict[name] weight_loader = param.weight_loader weight_loader( param, loaded_weight, name, shard_id=shard_id, expert_id=expert_id, ) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue if 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") @classmethod def get_model_config_for_expert_location(cls, config): return ModelConfigForExpertLocation( num_layers=config.num_hidden_layers, num_logical_experts=config.num_experts, num_groups=None, ) def set_eagle3_layers_to_capture(self, layer_ids: Optional[List[int]] = None): if not self.pp_group.is_last_rank: return self.capture_aux_hidden_states = True if layer_ids is None: num_layers = self.config.num_hidden_layers self.model.set_eagle3_layers_to_capture( [ 2, num_layers // 2, num_layers - 3, ] ) # Specific layers for EAGLE3 support else: self.model.set_eagle3_layers_to_capture([val + 1 for val in layer_ids]) EntryClass = Qwen2MoeForCausalLM