[Model] Add support for Nanbeige4.2 (#32151)

Co-authored-by: root <lizongqiang@kanzhun.com>
Co-authored-by: Xinyuan Tong <xinyuantong.cs@gmail.com>

python/sglang/srt/configs/__init__.py

@@ -52,6 +52,7 @@ from sglang.srt.configs.muse_glimmer import (
     MuseGlimmerAssistantConfig,
     MuseGlimmerConfig,
 )
+from sglang.srt.configs.nanbeige import NanbeigeConfig
 from sglang.srt.configs.nano_nemotron_vl import (
     NemotronH_Nano_Omni_Reasoning_V3_Config,
     NemotronH_Nano_VL_V2_Config,
@@ -130,6 +131,7 @@ __all__ = [
     "NemotronHPuzzleConfig",
     "NemotronH_Nano_VL_V2_Config",
     "NemotronH_Nano_Omni_Reasoning_V3_Config",
+    "NanbeigeConfig",
     "JetNemotronConfig",
     "JetVLMConfig",
     "MiniCPMHybridConfig",

python/sglang/srt/configs/model_config.py

@@ -1148,6 +1148,9 @@ class ModelConfig:
         if "IQuestLoopCoderForCausalLM" in self.hf_config.architectures:
             loop_num = getattr(self.hf_text_config, "loop_num", 1)
             self.num_attention_layers = int(self.num_hidden_layers * int(loop_num))
+        if "NanbeigeForCausalLM" in self.hf_config.architectures:
+            num_loops = getattr(self.hf_text_config, "num_loops", 1)
+            self.num_attention_layers = int(self.num_hidden_layers * int(num_loops))
         if "WhisperForConditionalGeneration" in self.hf_config.architectures:
             # Whisper has unique layer ID scheme:
             # - Encoder self-attention: 0 to encoder_layers-1 (no KV cache)

python/sglang/srt/configs/nanbeige.py

@@ -0,0 +1,217 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+"""Nanbeige model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class NanbeigeConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`NanbeigeModel`]. It is used to instantiate an Nanbeige
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LLaMA-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the Nanbeige model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`NanbeigeModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens,
+            Llama 2 up to 4096, CodeLlama up to 16384.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        num_loops (`int`, *optional*, defaults to 1):
+            The number of loops for the loop model.
+        loop_loss_weights (`List[float]`, *optional*, defaults to `[]`):
+            The weights for the loop loss.
+        skip_loop_final_norm (`bool`, *optional*, defaults to `False`):
+            Whether to skip final norm after each loop (except the last one).
+
+    ```python
+    >>> from configuration_nanbeige import NanbeigeConfig
+    >>> from modeling_nanbeige import NanbeigeModel
+
+    >>> # Initializing a Nanbeige nanbeige-7b style configuration
+    >>> configuration = NanbeigeConfig()
+
+    >>> # Initializing a model from the nanbeige-7b style configuration
+    >>> model = NanbeigeModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "nanbeige"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=166144,
+        hidden_size=3072,
+        intermediate_size=10752,
+        num_hidden_layers=22,
+        num_attention_heads=48,
+        num_key_value_heads=None,
+        head_dim=None,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=50000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        num_loops=2,
+        loop_loss_weights=None,
+        skip_loop_final_norm=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # Add head_dim logic
+        if head_dim is not None:
+            self.head_dim = head_dim
+        else:
+            self.head_dim = hidden_size // num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        self.num_loops = num_loops
+        self.loop_loss_weights = (
+            loop_loss_weights if loop_loss_weights is not None else []
+        )
+        self.skip_loop_final_norm = skip_loop_final_norm
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if (
+            rope_scaling_factor is None
+            or not isinstance(rope_scaling_factor, float)
+            or rope_scaling_factor <= 1.0
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}"
+            )

python/sglang/srt/function_call/function_call_parser.py

@@ -97,6 +97,7 @@ class FunctionCallParser:
         "step3p5": Qwen3CoderDetector,
         "minimax-m2": MinimaxM2Detector,
         "minimax-m3": MinimaxM3Detector,
+        "nanbeige": Qwen3CoderDetector,
         "trinity": TrinityDetector,
         "interns1": InternlmDetector,
         "hermes": HermesDetector,

python/sglang/srt/model_executor/model_runner_components/layer_setup.py

@@ -18,12 +18,23 @@ class AttentionAndMoeLayers(NamedTuple):
     mha_companion_layers: list[Any]
 
 
+def _get_loop_num(hf_config: Any) -> int:
+    # Nanbeige uses num_loops; IQuestLoopCoder uses loop_num.
+    return int(getattr(hf_config, "loop_num", getattr(hf_config, "num_loops", 1)) or 1)
+
+
 def compute_attention_and_moe_layers(layer_model: Any) -> AttentionAndMoeLayers:
     attention_layers: list[Any] = []
     moe_layers: list[Any] = []
     moe_fusions: list[Any] = []
     dsa_indexers: list[Any] = []
     mha_companion_layers: list[Any] = []
+
+    # Loop models (Nanbeige / IQuestLoopCoder) store one RadixAttention per loop
+    # in a ModuleList. Prefill CUDA graph indexes by layer_id, so expand and
+    # reorder to a dense [0..N) list.
+    has_loop_attn = False
+
     layers = layer_model.layers
     if isinstance(layers, nn.ModuleDict):
         layers = layers.values()
@@ -62,11 +73,16 @@ def compute_attention_and_moe_layers(layer_model: Any) -> AttentionAndMoeLayers:
                 # Mamba layer with split op support - store the layer itself
                 attn_layer = layer
 
-        # Keep these lists aligned with global layer ids. Pipeline-parallel
+        if isinstance(attn_layer, nn.ModuleList):
-        # models retain placeholders outside the local stage, while real
+            attention_layers.extend(attn_layer)
-        # attention modules use their global layer_id during graph replay.
+            mha_companion_layers.extend([mha_companion_layer] * len(attn_layer))
-        attention_layers.append(attn_layer)
+            has_loop_attn = True
-        mha_companion_layers.append(mha_companion_layer)
+        else:
+            # Keep these lists aligned with global layer ids. Pipeline-parallel
+            # models retain placeholders outside the local stage, while real
+            # attention modules use their global layer_id during graph replay.
+            attention_layers.append(attn_layer)
+            mha_companion_layers.append(mha_companion_layer)
 
         moe_block = None
         moe_fusion = None
@@ -93,6 +109,10 @@ def compute_attention_and_moe_layers(layer_model: Any) -> AttentionAndMoeLayers:
             dsa_indexer = layer.self_attn.indexer
         dsa_indexers.append(dsa_indexer)
 
+    # Reorder so attention_layers[i] matches RadixAttention.layer_id.
+    if has_loop_attn:
+        attention_layers.sort(key=lambda x: x.layer_id)
+
     return AttentionAndMoeLayers(
         attention_layers,
         moe_layers,
@@ -132,7 +152,7 @@ def resolve_layer_indices(
     num_effective_layers = pp_range.end_layer - pp_range.start_layer
 
     # For LoopCoder models, each loop has its own layer_id, so we need to multiply by loop_num
-    loop_num = getattr(model_config.hf_config, "loop_num", 1)
+    loop_num = _get_loop_num(model_config.hf_config)
     if loop_num > 1:
         num_effective_layers = num_effective_layers * loop_num
 

python/sglang/srt/model_executor/model_runner_components/spec_aux_hidden_state.py

@@ -158,6 +158,9 @@ def _resolve_dflash_aux_hidden_state(
                 f"in config. Got target={target_num_layers}."
             )
         target_num_layers = int(target_num_layers)
+        # Loop models: target layer ids span num_hidden_layers * num_loops.
+        num_loops = getattr(model_config.hf_text_config, "num_loops", 1)
+        target_num_layers = target_num_layers * int(num_loops)
 
         if (
             trained_target_layers is not None

python/sglang/srt/models/nanbeige.py

@@ -0,0 +1,626 @@
+import logging
+from typing import Iterable, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+
+from sglang.srt.configs import NanbeigeConfig
+from sglang.srt.distributed import get_pp_group
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.dp_attention import is_dp_attention_enabled
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.pooler import EmbeddingPoolerOutput, Pooler, PoolingType
+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.forward_batch_info import ForwardBatch, PPProxyTensors
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.runtime_context import get_parallel
+from sglang.srt.utils import add_prefix, make_layers
+
+logger = logging.getLogger(__name__)
+
+
+class NanbeigeRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        NanbeigeRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+class NanbeigeMLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> 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),
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=add_prefix("down_proj", prefix),
+        )
+        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, 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=use_reduce_scatter,
+        )
+        return x
+
+
+class NanbeigeAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(
+        self,
+        config: NanbeigeConfig,
+        layer_id: Optional[int] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.layer_id = layer_id
+        tp_size = get_parallel().tp_size
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+
+        self.total_num_heads = config.num_attention_heads
+        assert self.total_num_heads % tp_size == 0, (
+            "num_attention_heads must be divisible by tp_size."
+        )
+        self.num_heads = self.total_num_heads // tp_size
+        self.head_dim = getattr(
+            config, "head_dim", self.hidden_size // self.total_num_heads
+        )
+
+        self.total_num_kv_heads = config.num_key_value_heads
+        assert self.total_num_kv_heads >= tp_size, (
+            "num_key_value_heads must be greater than tp_size."
+        )
+        assert self.total_num_kv_heads % tp_size == 0, (
+            "num_key_value_heads must be divisible by tp_size."
+        )
+        self.num_kv_heads = config.num_key_value_heads // tp_size
+
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+        self.scaling = self.head_dim**-0.5
+        self.total_layers = config.num_hidden_layers
+        self.num_loops = config.num_loops
+
+        self.qkv_proj = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=add_prefix("qkv_proj", prefix),
+        )
+
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            self.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=add_prefix("o_proj", prefix),
+        )
+
+        self.attn = nn.ModuleList()
+        base_layer_id = layer_id
+        for loop_idx in range(self.num_loops):
+            layer_id = base_layer_id + loop_idx * self.total_layers
+            self.attn.append(
+                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),
+                )
+            )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=self.max_position_embeddings,
+            base=self.rope_theta,
+            rope_scaling=self.config.rope_scaling,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+        loop_idx: int,
+    ) -> 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[loop_idx](q, k, v, forward_batch)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class NanbeigeDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: NanbeigeConfig,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        alt_stream: Optional[torch.cuda.Stream] = None,
+    ) -> None:
+        super().__init__()
+
+        self.self_attn = NanbeigeAttention(
+            config=config,
+            layer_id=layer_id,
+            quant_config=quant_config,
+            prefix=add_prefix("self_attn", prefix),
+        )
+
+        self.mlp = NanbeigeMLP(
+            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
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+        loop_idx: int,
+        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)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            forward_batch=forward_batch,
+            loop_idx=loop_idx,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+
+        return hidden_states, residual
+
+
+class NanbeigeModel(nn.Module):
+    def __init__(
+        self,
+        config: NanbeigeConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        decoder_layer_type: type[nn.Module] = NanbeigeDecoderLayer,
+        alt_stream: Optional[torch.cuda.Stream] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.vocab_size = config.vocab_size
+        self.pp_group = get_pp_group()
+        pp_size = self.pp_group.world_size
+        assert pp_size == 1, (
+            "The NanbeigeModel only supports a pipeline parallelism (PP) value of 1."
+        )
+
+        if self.pp_group.is_first_rank:
+            self.embed_tokens = VocabParallelEmbedding(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                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 NanbeigeDecoderLayer
+        decoder_layer_type = decoder_layer_type or NanbeigeDecoderLayer
+        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 / DFLASH: capture *before* unrolled layer id (ids already +1'd).
+        self.layers_to_capture = []
+
+    def get_input_embedding(self, input_ids):
+        return self.embed_tokens(input_ids)
+
+    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 = []
+        # Draft target_layer_ids are in unrolled depth space:
+        #   logical_id = loop_idx * num_hidden_layers + physical_i
+        # e.g. num_hidden_layers=22, num_loops=2 → ids in [0, 44).
+        # DFLASH style: setter stores k+1; we capture before that unrolled step.
+        num_physical_layers = self.config.num_hidden_layers
+        for loop_idx in range(self.config.num_loops):
+            for i in range(self.start_layer, self.end_layer):
+                logical_id = loop_idx * num_physical_layers + i
+                if logical_id in self.layers_to_capture:
+                    aux_hidden_states.append(
+                        hidden_states + residual
+                        if residual is not None
+                        else hidden_states.clone()
+                    )
+                layer = self.layers[i]
+                hidden_states, residual = layer(
+                    positions,
+                    hidden_states,
+                    forward_batch,
+                    loop_idx,
+                    residual,
+                )
+
+            # Match the reference HF semantics for Nanbeige "loop models":
+            # - At the end of each full loop (except the last), convert the
+            #   (hidden_states, residual) representation into real hidden_states
+            #   by applying the missing residual addition.
+            # - If skip_loop_final_norm=False, HF applies RMSNorm after each loop
+            #   (including intermediate loops) before entering the next loop.
+            if loop_idx != self.config.num_loops - 1:
+                if residual is not None:
+                    hidden_states = hidden_states + residual
+                    residual = None
+                if not self.config.skip_loop_final_norm:
+                    hidden_states = self.norm(hidden_states)
+
+        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 NanbeigeForCausalLM(nn.Module):
+    def __init__(
+        self,
+        config: NanbeigeConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.pp_group = get_pp_group()
+        self.config = config
+        self.quant_config = quant_config
+        self.model = NanbeigeModel(
+            config, quant_config=quant_config, prefix=add_prefix("model", prefix)
+        )
+
+        # handle the lm head on different pp ranks
+        if self.pp_group.is_last_rank:
+            if self.pp_group.world_size == 1 and 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),
+                )
+        else:
+            # ranks other than the last rank will have a placeholder layer
+            self.lm_head = PPMissingLayer()
+
+        # perform weight tying for PP
+        if self.pp_group.world_size > 1 and config.tie_word_embeddings:
+            if self.pp_group.is_first_rank:
+                self.pp_group.send(
+                    self.model.embed_tokens.weight, dst=self.pp_group.last_rank
+                )
+            else:
+                emb_token_weight = self.pp_group.recv(
+                    size=(config.vocab_size, config.hidden_size),
+                    dtype=next(self.model.parameters()).dtype,
+                    src=self.pp_group.first_rank,
+                )
+                self.lm_head.weight.copy_(emb_token_weight)
+
+        self.logits_processor = LogitsProcessor(config)
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
+        # For EAGLE3 support
+        self.capture_aux_hidden_states = False
+
+    def get_input_embedding(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embedding(input_ids)
+
+    def get_input_embeddings(self) -> nn.Embedding:
+        return self.model.embed_tokens
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+        get_embedding: bool = False,
+        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:
+            if not get_embedding:
+                return self.logits_processor(
+                    input_ids,
+                    hidden_states,
+                    self.lm_head,
+                    forward_batch,
+                    aux_hidden_states,
+                )
+            else:
+                return self.pooler(hidden_states, forward_batch)
+        else:
+            return hidden_states
+
+    @property
+    def start_layer(self):
+        return self.model.start_layer
+
+    @property
+    def end_layer(self):
+        return self.model.end_layer
+
+    @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,
+    ):
+        assert False, "NanbeigeModel does not support split_prefill."
+
+    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", ".up_proj", 1),
+            (".gate_up_proj", ".gate_proj", 0),
+        ]
+
+        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 or "projector" in name:
+                continue
+            if self.config.tie_word_embeddings and "lm_head.weight" in name:
+                if self.pp_group.world_size > 1 and self.pp_group.is_last_rank:
+                    # Handle pp weight tying here
+                    # find the embed_tokens.weight in the weights
+                    embed_token_weights = next(
+                        filter(lambda x: x[0] == "model.embed_tokens.weight", weights)
+                    )[1]
+                    loaded_weight = embed_token_weights
+                else:
+                    continue
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not 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:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and 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")
+
+    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 set_dflash_layers_to_capture(self, layer_ids: List[int]):
+        if not self.pp_group.is_last_rank:
+            return
+
+        if layer_ids is None:
+            raise ValueError(
+                "DFLASH requires explicit layer_ids for aux hidden capture."
+            )
+
+        self.capture_aux_hidden_states = True
+        # Unrolled ids: capture before step (k+1) == after HF-style layer k.
+        self.model.layers_to_capture = [val + 1 for val in layer_ids]
+
+
+class NanbeigeForSequenceClassification(nn.Module):
+    def __init__(
+        self,
+        config: NanbeigeConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.model = NanbeigeModel(
+            config, quant_config=quant_config, prefix=add_prefix("model", prefix)
+        )
+        self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=False)
+
+        self.eos_token_id = config.eos_token_id
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+        get_embedding: bool = True,
+    ) -> EmbeddingPoolerOutput:
+        assert get_embedding, (
+            "NanbeigeForSequenceClassification is only used for embedding"
+        )
+
+        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
+        logits = self.score(hidden_states)
+        pooled_logits = self.pooler(logits, forward_batch).embeddings
+
+        return EmbeddingPoolerOutput(pooled_logits)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        # Filter out lm_head weights of NanbeigeForCausalLM
+        filtered_weights = [
+            (name, w) for name, w in weights if not name.startswith("lm_head")
+        ]
+        return NanbeigeForCausalLM.load_weights(self, filtered_weights)
+
+
+EntryClass = [NanbeigeForCausalLM, NanbeigeForSequenceClassification]

python/sglang/srt/parser/reasoning_parser.py

@@ -2042,6 +2042,7 @@ class ReasoningParser:
         "minimax": Qwen3Detector,
         "minimax-append-think": MiniMaxAppendThinkDetector,
         "minimax-m3": MiniMaxM3Detector,
+        "nanbeige": Qwen3Detector,
         "step3": DeepSeekR1Detector,
         "step3p5": DeepSeekR1Detector,
         "mistral": MistralDetector,

python/sglang/srt/utils/hf_transformers/common.py

@@ -63,6 +63,7 @@ from sglang.srt.configs import (
     MultiModalityConfig,
     MuseGlimmerAssistantConfig,
     MuseGlimmerConfig,
+    NanbeigeConfig,
     NemotronH_Nano_Omni_Reasoning_V3_Config,
     NemotronH_Nano_VL_V2_Config,
     NemotronHConfig,
@@ -131,6 +132,7 @@ _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
         NemotronH_Nano_Omni_Reasoning_V3_Config,
         NemotronHConfig,
         NemotronHPuzzleConfig,
+        NanbeigeConfig,
         DeepseekVLV2Config,
         Qwen3_5Config,
         Qwen3_5MoeConfig,