From c04efe030accffafbba596e9f17a399b775830af Mon Sep 17 00:00:00 2001 From: Changhun Lee Date: Fri, 30 Jan 2026 21:01:14 +0900 Subject: [PATCH] [Model] Add K-EXAONE model support (#16294) Signed-off-by: lkm2835 Co-authored-by: lgai-exaone Co-authored-by: lkm2835 Co-authored-by: Xinyuan Tong --- python/sglang/srt/configs/model_config.py | 4 + python/sglang/srt/models/exaone_moe.py | 881 +++++++++++++++++++++ python/sglang/srt/models/exaone_moe_mtp.py | 106 +++ python/sglang/srt/server_args.py | 16 +- 4 files changed, 1000 insertions(+), 7 deletions(-) create mode 100755 python/sglang/srt/models/exaone_moe.py create mode 100644 python/sglang/srt/models/exaone_moe_mtp.py diff --git a/python/sglang/srt/configs/model_config.py b/python/sglang/srt/configs/model_config.py index edb183d84..32032bded 100644 --- a/python/sglang/srt/configs/model_config.py +++ b/python/sglang/srt/configs/model_config.py @@ -312,6 +312,10 @@ class ModelConfig: self.hf_config.architectures[0] = "Qwen3NextForCausalLMMTP" self.hf_config.num_nextn_predict_layers = 1 + if is_draft_model and self.hf_config.architectures[0] == "ExaoneMoEForCausalLM": + self.hf_config.architectures[0] = "ExaoneMoEForCausalLMMTP" + self.hf_config.num_nextn_predict_layers = 1 + if is_draft_model and self.hf_config.architectures[0] == "NemotronHForCausalLM": self.hf_config.architectures[0] = "NemotronHForCausalLMMTP" self.hf_config.num_nextn_predict_layers = 1 diff --git a/python/sglang/srt/models/exaone_moe.py b/python/sglang/srt/models/exaone_moe.py new file mode 100755 index 000000000..ab31c0a59 --- /dev/null +++ b/python/sglang/srt/models/exaone_moe.py @@ -0,0 +1,881 @@ +# Copyright 2025 The LG AI Research Team +# 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 the vLLM version of EXAONE-MoE model +"""Inference-only ExaoneMoE model compatible with HuggingFace weights.""" + +import logging +from collections.abc import Iterable +from typing import Any, Dict, Optional, Tuple, Union + +import torch +from torch import nn +from transformers import PretrainedConfig + +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.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, LogitsProcessorOutput +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 +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 +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 LazyValue, add_prefix, is_cuda, make_layers + +logger = logging.getLogger(__name__) + +_is_cuda = is_cuda() + + +class ExaoneMoEMLP(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__() + gateup_quant_config = quant_config + down_quant_config = quant_config + if quant_config and hasattr(quant_config, "ignore") and quant_config.ignore: + if add_prefix("gate_proj", prefix) in quant_config.ignore: + gateup_quant_config = None + if add_prefix("down_proj", prefix) in quant_config.ignore: + down_quant_config = None + + self.gate_up_proj = MergedColumnParallelLinear( + hidden_size, + [intermediate_size] * 2, + bias=False, + quant_config=gateup_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=down_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, + ): + 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 ExaoneMoESparseMoEBlock(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.moe_ep_size = get_moe_expert_parallel_world_size() + self.layer_id = layer_id + self.routed_scaling_factor = config.routed_scaling_factor + self.alt_stream = alt_stream + + self.n_routed_experts = config.num_experts + + 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.gate = ReplicatedLinear( + config.hidden_size, + config.num_experts, + bias=False, + quant_config=None, + prefix=add_prefix("gate", prefix), + ) + + self.e_score_correction_bias = nn.Parameter( + torch.empty(config.num_experts, dtype=torch.float32) + ) + + self.experts = get_moe_impl_class(quant_config)( + num_experts=config.num_experts + + get_global_server_args().ep_num_redundant_experts, + top_k=config.num_experts_per_tok, + hidden_size=config.hidden_size, + intermediate_size=config.moe_intermediate_size, + layer_id=self.layer_id, + quant_config=quant_config, + prefix=add_prefix("experts", prefix), + routing_method_type=RoutingMethodType.RenormalizeNaive, + ) + + self.topk = TopK( + top_k=config.num_experts_per_tok, + renormalize=config.norm_topk_prob, + use_grouped_topk=True, + num_expert_group=config.n_group, + topk_group=config.topk_group, + correction_bias=self.e_score_correction_bias, + routed_scaling_factor=self.routed_scaling_factor, + apply_routed_scaling_factor_on_output=True, + scoring_func="sigmoid", + ) + + if config.num_shared_experts is not None: + intermediate_size = config.moe_intermediate_size * config.num_shared_experts + self.shared_experts = ExaoneMoEMLP( + 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() + else {} + ), + ) + + if get_moe_a2a_backend().is_deepep(): + 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"] + ] + + def _forward_shared_experts(self, hidden_states: torch.Tensor) -> torch.Tensor: + shared_output = self.shared_experts(hidden_states) + 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, _ = 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) -> torch.Tensor: + 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: + final_hidden_states = 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 ExaoneMoEAttention(nn.Module): + def __init__( + self, + config: PretrainedConfig, + hidden_size: int, + num_heads: int, + num_kv_heads: int, + layer_id: int = 0, + rope_theta: float = 1000000, + rope_scaling: Optional[Dict[str, Any]] = None, + rope_is_neox_style: bool = True, + max_position_embeddings: int = 8192, + quant_config: Optional[QuantizationConfig] = None, + bias: bool = False, + 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) + # MistralConfig has an optional head_dim introduced by Mistral-Nemo + self.head_dim = getattr( + config, "head_dim", self.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.max_position_embeddings = max_position_embeddings + + qkv_quant_config = quant_config + o_quant_config = quant_config + if quant_config and hasattr(quant_config, "ignore") and quant_config.ignore: + if add_prefix("q_proj", prefix) in quant_config.ignore: + qkv_quant_config = None + if add_prefix("o_proj", prefix) in quant_config.ignore: + o_quant_config = None + + self.qkv_proj = QKVParallelLinear( + hidden_size, + self.head_dim, + self.total_num_heads, + self.total_num_kv_heads, + bias=bias, + quant_config=qkv_quant_config, + prefix=add_prefix("qkv_proj", prefix), + tp_rank=attn_tp_rank, + tp_size=attn_tp_size, + ) + self.o_proj = RowParallelLinear( + self.total_num_heads * self.head_dim, + hidden_size, + bias=bias, + quant_config=o_quant_config, + prefix=add_prefix("o_proj", prefix), + tp_rank=attn_tp_rank, + tp_size=attn_tp_size, + ) + + self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps) + self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps) + + if quant_config is not None and quant_config.get_name() == "gguf": + rope_is_neox_style = False + + self.sliding_window = config.layer_types[layer_id] == "sliding_attention" + + # apply rotary embeddings to every layer in full attention models + self.apply_rope_all_layers = "sliding_attention" not in config.layer_types + + 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, + is_neox_style=rope_is_neox_style, + ) + + self.attn = RadixAttention( + self.num_heads, + self.head_dim, + self.scaling, + num_kv_heads=self.num_kv_heads, + layer_id=layer_id, + prefix=add_prefix("attn", prefix), + sliding_window_size=( + config.sliding_window if self.sliding_window else None + ), + ) + self.layer_id = layer_id + + 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 = q.reshape(-1, self.head_dim) + q = self.q_norm(q) + q = q.reshape(-1, self.num_heads * self.head_dim) + + k = k.reshape(-1, self.head_dim) + k = self.k_norm(k) + k = k.reshape(-1, self.num_kv_heads * self.head_dim) + + if self.sliding_window or self.apply_rope_all_layers: + 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 ExaoneMoEDecoderLayer(nn.Module): + def __init__( + self, + config: PretrainedConfig, + layer_id: int = 0, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + alt_stream: Optional[torch.cuda.Stream] = None, + ) -> None: + super().__init__() + self.hidden_size = config.hidden_size + self.config = config + rope_theta = getattr(config, "rope_theta", 1000000) + rope_scaling = getattr(config, "rope_scaling", None) + if rope_scaling is not None and getattr( + config, "original_max_position_embeddings", None + ): + rope_scaling["original_max_position_embeddings"] = ( + config.original_max_position_embeddings + ) + rope_is_neox_style = getattr(config, "rope_is_neox_style", True) + max_position_embeddings = getattr(config, "max_position_embeddings", 131072) + + attention_bias = getattr(config, "attention_bias", False) or getattr( + config, "bias", False + ) + self.attn_tp_size = get_attention_tp_size() + self.attn_tp_rank = get_attention_tp_rank() + + self.self_attn = ExaoneMoEAttention( + config=config, + 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, + rope_is_neox_style=rope_is_neox_style, + max_position_embeddings=max_position_embeddings, + quant_config=quant_config, + bias=attention_bias, + prefix=add_prefix("self_attn", prefix), + ) + + if config.is_moe_layer[layer_id]: + self.mlp = ExaoneMoESparseMoEBlock( + layer_id=layer_id, + config=config, + quant_config=quant_config, + alt_stream=alt_stream, + prefix=add_prefix("mlp", prefix), + ) + else: + self.mlp = ExaoneMoEMLP( + hidden_size=self.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 + ) + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + forward_batch: ForwardBatch, + residual: Optional[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor]: + if residual is None: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + else: + hidden_states, residual = self.input_layernorm(hidden_states, residual) + + # Self Attention + hidden_states = self.self_attn( + positions=positions, + hidden_states=hidden_states, + forward_batch=forward_batch, + ) + + hidden_states, residual = self.post_attention_layernorm(hidden_states, residual) + # Fully Connected + hidden_states = self.mlp(hidden_states) + + return hidden_states, residual + + +class ExaoneMoEModel(nn.Module): + fall_back_to_pt_during_load = False + + def __init__( + self, + config: PretrainedConfig, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + 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, + enable_tp=not is_dp_attention_enabled(), + ) + else: + self.embed_tokens = PPMissingLayer() + + self.layers, self.start_layer, self.end_layer = make_layers( + config.num_hidden_layers, + lambda idx, prefix: ExaoneMoEDecoderLayer( + 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 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 = [] + for i in range(self.start_layer, self.end_layer): + with get_global_expert_distribution_recorder().with_current_layer(i): + if i in self.layers_to_capture: + aux_hidden_states.append(hidden_states + residual) + layer = self.layers[i] + hidden_states, residual = layer( + positions, hidden_states, forward_batch, residual + ) + 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 ExaoneMoEForCausalLM(nn.Module): + 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 = ExaoneMoEModel( + config, + quant_config=quant_config, + prefix=add_prefix("model", prefix), + alt_stream=alt_stream, + ) + if self.config.tie_word_embeddings: + self.lm_head = self.model.embed_tokens + else: + 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 + + self._routed_experts_weights_of_layer = LazyValue( + lambda: { + layer_id: self.model.layers[layer_id].mlp.get_moe_weights() + for layer_id in range(self.start_layer, self.end_layer) + if isinstance(self.model.layers[layer_id].mlp, ExaoneMoESparseMoEBlock) + } + ) + + @property + def routed_experts_weights_of_layer(self): + return self._routed_experts_weights_of_layer.value + + @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, + ) -> LogitsProcessorOutput: + 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): + 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 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() + + def load_weights( + self, weights: Iterable[Tuple[str, torch.Tensor]], is_mtp: bool = False + ): + 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: + if is_mtp: + if "mtp" not in name: + continue + if name in [ + "mtp.fc.weight", + "mtp.pre_fc_norm_embedding.weight", + "mtp.pre_fc_norm_hidden.weight", + ]: + name = name.replace("mtp.", "") + else: + name = name.replace("mtp", "model") + + if not is_mtp and "mtp" in name: + continue + + if "rotary_emb.inv_freq" in name or "projector" in name: + continue + if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name: + # Models trained using ColossalAI may include these tensors in + # the checkpoint. Skip them. + continue + if name.startswith("model.vision_tower") and name not in params_dict: + continue + + for param_name, weight_name, shard_id in stacked_params_mapping: + if weight_name not in name: + continue + 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, + expert_id=expert_id, + shard_id=shard_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 get_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.layers_to_capture = [ + 2, + num_layers // 2, + num_layers - 3, + ] # Specific layers for EAGLE3 support + else: + self.model.layers_to_capture = [val + 1 for val in layer_ids] + + +EntryClass = ExaoneMoEForCausalLM diff --git a/python/sglang/srt/models/exaone_moe_mtp.py b/python/sglang/srt/models/exaone_moe_mtp.py new file mode 100644 index 000000000..05e63dcae --- /dev/null +++ b/python/sglang/srt/models/exaone_moe_mtp.py @@ -0,0 +1,106 @@ +# Copyright 2025 The LG AI Research Team +# 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 the vLLM version of EXAONE-MoE MTP +"""Inference-only ExaoneMoE MTP Speculative Decoding.""" + +import logging +from typing import Iterable, Optional, Tuple + +import torch +from torch import nn +from transformers import PretrainedConfig + +from sglang.srt.distributed import get_pp_group, get_tensor_model_parallel_world_size +from sglang.srt.layers.layernorm import RMSNorm +from sglang.srt.layers.logits_processor import LogitsProcessor +from sglang.srt.layers.quantization.base_config import QuantizationConfig +from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead +from sglang.srt.model_executor.forward_batch_info import ForwardBatch +from sglang.srt.models.exaone_moe import ExaoneMoEForCausalLM, ExaoneMoEModel +from sglang.srt.server_args import get_global_server_args +from sglang.srt.utils import add_prefix + +logger = logging.getLogger(__name__) + + +class ExaoneMoEForCausalLMMTP(ExaoneMoEForCausalLM): + def __init__( + self, + config: PretrainedConfig, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: + nn.Module.__init__(self) + self.config = config + config.num_hidden_layers = 1 + self.tp_size = get_tensor_model_parallel_world_size() + self.quant_config = quant_config + self.pp_group = get_pp_group() + + self.fc = nn.Linear(2 * config.hidden_size, config.hidden_size, bias=False) + self.pre_fc_norm_embedding = RMSNorm( + config.hidden_size, eps=config.rms_norm_eps + ) + self.pre_fc_norm_hidden = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.model = ExaoneMoEModel( + 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) + + @torch.no_grad() + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + forward_batch: ForwardBatch, + input_embeds: Optional[torch.Tensor] = None, + **kwargs, + ): + if input_embeds is None: + input_embeds = self.model.embed_tokens(input_ids) + + hidden_states = forward_batch.spec_info.hidden_states + + if not forward_batch.forward_mode.is_idle(): + input_embeds = self.pre_fc_norm_embedding(input_embeds) + hidden_states = self.pre_fc_norm_hidden(hidden_states) + hidden_states = self.fc(torch.cat((input_embeds, hidden_states), dim=-1)) + + hidden_states = self.model( + input_ids, + positions, + forward_batch, + hidden_states, + ) + + return self.logits_processor( + input_ids, hidden_states, self.lm_head, forward_batch + ) + + def load_weights( + self, weights: Iterable[Tuple[str, torch.Tensor]], is_mtp: bool = False + ): + super().load_weights(weights, is_mtp=True) + + +EntryClass = ExaoneMoEForCausalLMMTP diff --git a/python/sglang/srt/server_args.py b/python/sglang/srt/server_args.py index 294b6d77b..ee1b37add 100644 --- a/python/sglang/srt/server_args.py +++ b/python/sglang/srt/server_args.py @@ -1446,15 +1446,17 @@ class ServerArgs: f"Disable hybrid SWA memory for {model_arch} as it is not yet supported." ) self.disable_hybrid_swa_memory = True - elif model_arch in ["Exaone4ForCausalLM"]: + elif model_arch in ["Exaone4ForCausalLM", "ExaoneMoEForCausalLM"]: if hf_config.sliding_window_pattern is not None: - # https://docs.sglang.ai/advanced_features/attention_backend.html - assert self.attention_backend in { - "fa3", - "triton", - "trtllm_mha", - }, "fa3, triton, or trtllm_mla is required for Exaone4ForCausalLM-32B" + logger.warning( + f"Disabling hybrid SWA memory for {model_arch} as it is not yet supported." + ) self.disable_hybrid_swa_memory = True + # https://docs.sglang.ai/advanced_features/attention_backend.html + accepted_backends = ["fa3", "triton", "trtllm_mha"] + assert ( + self.attention_backend in accepted_backends + ), f"One of the attention backends in {accepted_backends} is required for {model_arch}, but got {self.attention_backend}" elif model_arch in ["Olmo2ForCausalLM"]: # FIXME: https://github.com/sgl-project/sglang/pull/7367 is not compatible with Olmo3 model. logger.warning(