[Model] Add K-EXAONE model support (#16294)
Signed-off-by: lkm2835 <lkm2835@gmail.com> Co-authored-by: lgai-exaone <exaonemodels@lgresearch.ai> Co-authored-by: lkm2835 <lkm2835@gmail.com> Co-authored-by: Xinyuan Tong <xinyuantong.cs@gmail.com>
This commit is contained in:
Changhun Lee
@@ -312,6 +312,10 @@ class ModelConfig:
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self.hf_config.architectures[0] = "Qwen3NextForCausalLMMTP"
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self.hf_config.num_nextn_predict_layers = 1
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if is_draft_model and self.hf_config.architectures[0] == "ExaoneMoEForCausalLM":
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self.hf_config.architectures[0] = "ExaoneMoEForCausalLMMTP"
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self.hf_config.num_nextn_predict_layers = 1
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if is_draft_model and self.hf_config.architectures[0] == "NemotronHForCausalLM":
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self.hf_config.architectures[0] = "NemotronHForCausalLMMTP"
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self.hf_config.num_nextn_predict_layers = 1
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881
python/sglang/srt/models/exaone_moe.py
Executable file
881
python/sglang/srt/models/exaone_moe.py
Executable file
@@ -0,0 +1,881 @@
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# Copyright 2025 The LG AI Research Team
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# Copyright 2023-2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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# Adapted from the vLLM version of EXAONE-MoE model
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"""Inference-only ExaoneMoE model compatible with HuggingFace weights."""
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import logging
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from collections.abc import Iterable
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from typing import Any, Dict, Optional, Tuple, Union
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from sglang.srt.distributed import (
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get_moe_expert_parallel_world_size,
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get_pp_group,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce,
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)
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from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
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from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation
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from sglang.srt.eplb.expert_location_dispatch import ExpertLocationDispatchInfo
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from sglang.srt.layers.activation import SiluAndMul
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from sglang.srt.layers.dp_attention import (
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get_attention_tp_rank,
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get_attention_tp_size,
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is_dp_attention_enabled,
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)
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from sglang.srt.layers.layernorm import RMSNorm
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from sglang.srt.layers.linear import (
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MergedColumnParallelLinear,
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
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from sglang.srt.layers.moe import get_moe_a2a_backend
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from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
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from sglang.srt.layers.moe.topk import TopK
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from sglang.srt.layers.moe.utils import RoutingMethodType
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.layers.rotary_embedding import get_rope
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from sglang.srt.layers.utils import PPMissingLayer
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from sglang.srt.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.server_args import get_global_server_args
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from sglang.srt.utils import LazyValue, add_prefix, is_cuda, make_layers
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logger = logging.getLogger(__name__)
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_is_cuda = is_cuda()
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class ExaoneMoEMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: Optional[QuantizationConfig] = None,
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reduce_results: bool = True,
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prefix: str = "",
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tp_rank: Optional[int] = None,
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tp_size: Optional[int] = None,
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) -> None:
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super().__init__()
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gateup_quant_config = quant_config
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down_quant_config = quant_config
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if quant_config and hasattr(quant_config, "ignore") and quant_config.ignore:
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if add_prefix("gate_proj", prefix) in quant_config.ignore:
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gateup_quant_config = None
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if add_prefix("down_proj", prefix) in quant_config.ignore:
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down_quant_config = None
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size,
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[intermediate_size] * 2,
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bias=False,
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quant_config=gateup_quant_config,
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prefix=add_prefix("gate_up_proj", prefix),
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tp_rank=tp_rank,
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tp_size=tp_size,
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)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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quant_config=down_quant_config,
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reduce_results=reduce_results,
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prefix=add_prefix("down_proj", prefix),
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tp_rank=tp_rank,
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tp_size=tp_size,
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)
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if hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {hidden_act}. "
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"Only silu is supported for now."
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)
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self.act_fn = SiluAndMul()
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def forward(
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self,
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x,
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forward_batch=None,
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should_allreduce_fusion: bool = False,
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use_reduce_scatter: bool = False,
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):
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(
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x,
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skip_all_reduce=should_allreduce_fusion or use_reduce_scatter,
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)
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return x
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class ExaoneMoESparseMoEBlock(nn.Module):
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def __init__(
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self,
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layer_id: int,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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alt_stream: Optional[torch.cuda.Stream] = None,
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prefix: str = "",
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):
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super().__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.moe_ep_size = get_moe_expert_parallel_world_size()
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self.layer_id = layer_id
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self.routed_scaling_factor = config.routed_scaling_factor
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self.alt_stream = alt_stream
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self.n_routed_experts = config.num_experts
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if self.tp_size > config.num_experts:
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raise ValueError(
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f"Tensor parallel size {self.tp_size} is greater than "
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f"the number of experts {config.num_experts}."
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)
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self.gate = ReplicatedLinear(
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config.hidden_size,
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config.num_experts,
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bias=False,
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quant_config=None,
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prefix=add_prefix("gate", prefix),
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)
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self.e_score_correction_bias = nn.Parameter(
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torch.empty(config.num_experts, dtype=torch.float32)
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)
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self.experts = get_moe_impl_class(quant_config)(
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num_experts=config.num_experts
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+ get_global_server_args().ep_num_redundant_experts,
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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intermediate_size=config.moe_intermediate_size,
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layer_id=self.layer_id,
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quant_config=quant_config,
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prefix=add_prefix("experts", prefix),
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routing_method_type=RoutingMethodType.RenormalizeNaive,
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)
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self.topk = TopK(
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top_k=config.num_experts_per_tok,
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renormalize=config.norm_topk_prob,
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use_grouped_topk=True,
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num_expert_group=config.n_group,
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topk_group=config.topk_group,
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correction_bias=self.e_score_correction_bias,
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routed_scaling_factor=self.routed_scaling_factor,
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apply_routed_scaling_factor_on_output=True,
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scoring_func="sigmoid",
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)
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if config.num_shared_experts is not None:
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intermediate_size = config.moe_intermediate_size * config.num_shared_experts
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self.shared_experts = ExaoneMoEMLP(
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hidden_size=config.hidden_size,
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intermediate_size=intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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reduce_results=False,
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prefix=add_prefix("shared_experts", prefix),
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**(
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dict(tp_rank=0, tp_size=1)
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if get_moe_a2a_backend().is_deepep()
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else {}
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),
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)
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if get_moe_a2a_backend().is_deepep():
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self.ep_size = get_moe_expert_parallel_world_size()
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self.num_experts = (
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config.num_experts + get_global_server_args().ep_num_redundant_experts
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)
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self.top_k = config.num_experts_per_tok
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def get_moe_weights(self):
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return [
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x.data
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for name, x in self.experts.named_parameters()
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if name not in ["correction_bias"]
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]
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def _forward_shared_experts(self, hidden_states: torch.Tensor) -> torch.Tensor:
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shared_output = self.shared_experts(hidden_states)
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return shared_output
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def _forward_deepep(self, hidden_states: torch.Tensor, forward_batch: ForwardBatch):
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shared_output = None
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if hidden_states.shape[0] > 0:
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router_logits, _ = self.gate(hidden_states)
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shared_output = self._forward_shared_experts(hidden_states)
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topk_output = self.topk(
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hidden_states,
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router_logits,
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num_token_non_padded=forward_batch.num_token_non_padded,
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expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
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layer_id=self.layer_id,
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),
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)
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else:
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topk_output = self.topk.empty_topk_output(hidden_states.device)
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final_hidden_states = self.experts(
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hidden_states=hidden_states,
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topk_output=topk_output,
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)
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if shared_output is not None:
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final_hidden_states.add_(shared_output)
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return final_hidden_states
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def _forward_router_experts(self, hidden_states: torch.Tensor) -> torch.Tensor:
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router_logits, _ = self.gate(hidden_states)
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topk_output = self.topk(hidden_states, router_logits)
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return self.experts(hidden_states, topk_output)
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def forward_normal_dual_stream(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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current_stream = torch.cuda.current_stream()
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self.alt_stream.wait_stream(current_stream)
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shared_output = self._forward_shared_experts(hidden_states.clone())
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with torch.cuda.stream(self.alt_stream):
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router_output = self._forward_router_experts(hidden_states)
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current_stream.wait_stream(self.alt_stream)
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return router_output, shared_output
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def forward(
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self,
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hidden_states: torch.Tensor,
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forward_batch: Optional[ForwardBatch] = None,
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use_reduce_scatter: bool = False,
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) -> torch.Tensor:
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num_tokens, hidden_dim = hidden_states.shape
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hidden_states = hidden_states.view(-1, hidden_dim)
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if get_moe_a2a_backend().is_deepep():
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return self._forward_deepep(hidden_states, forward_batch)
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if (
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self.alt_stream is not None
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and hidden_states.shape[0] > 0
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and get_is_capture_mode()
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):
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final_hidden_states, shared_output = self.forward_normal_dual_stream(
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hidden_states
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)
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else:
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shared_output = self._forward_shared_experts(hidden_states)
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final_hidden_states = self._forward_router_experts(hidden_states)
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if shared_output is not None:
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final_hidden_states = final_hidden_states + shared_output
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if self.tp_size > 1 and not use_reduce_scatter:
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final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
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return final_hidden_states.view(num_tokens, hidden_dim)
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class ExaoneMoEAttention(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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layer_id: int = 0,
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rope_theta: float = 1000000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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rope_is_neox_style: bool = True,
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max_position_embeddings: int = 8192,
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quant_config: Optional[QuantizationConfig] = None,
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bias: bool = False,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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attn_tp_rank = get_attention_tp_rank()
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attn_tp_size = get_attention_tp_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % attn_tp_size == 0
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self.num_heads = self.total_num_heads // attn_tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= attn_tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % attn_tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert attn_tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // attn_tp_size)
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# MistralConfig has an optional head_dim introduced by Mistral-Nemo
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self.head_dim = getattr(
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config, "head_dim", self.hidden_size // self.total_num_heads
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)
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.max_position_embeddings = max_position_embeddings
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qkv_quant_config = quant_config
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o_quant_config = quant_config
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if quant_config and hasattr(quant_config, "ignore") and quant_config.ignore:
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if add_prefix("q_proj", prefix) in quant_config.ignore:
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qkv_quant_config = None
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if add_prefix("o_proj", prefix) in quant_config.ignore:
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o_quant_config = None
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=bias,
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quant_config=qkv_quant_config,
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prefix=add_prefix("qkv_proj", prefix),
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tp_rank=attn_tp_rank,
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tp_size=attn_tp_size,
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=bias,
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quant_config=o_quant_config,
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prefix=add_prefix("o_proj", prefix),
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tp_rank=attn_tp_rank,
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tp_size=attn_tp_size,
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)
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self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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if quant_config is not None and quant_config.get_name() == "gguf":
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rope_is_neox_style = False
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self.sliding_window = config.layer_types[layer_id] == "sliding_attention"
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# apply rotary embeddings to every layer in full attention models
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self.apply_rope_all_layers = "sliding_attention" not in config.layer_types
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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is_neox_style=rope_is_neox_style,
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)
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self.attn = RadixAttention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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layer_id=layer_id,
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prefix=add_prefix("attn", prefix),
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sliding_window_size=(
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config.sliding_window if self.sliding_window else None
|
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),
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)
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self.layer_id = layer_id
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|
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def forward(
|
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self,
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positions: torch.Tensor,
|
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hidden_states: torch.Tensor,
|
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forward_batch: ForwardBatch,
|
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) -> torch.Tensor:
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||||
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
|
||||
106
python/sglang/srt/models/exaone_moe_mtp.py
Normal file
106
python/sglang/srt/models/exaone_moe_mtp.py
Normal file
@@ -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
|
||||
@@ -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(
|
||||
|
||||
Reference in New Issue
Block a user