[Model] Support Meituan LongCat-Flash && LongCat-Flash-MTP (#9824)

python/sglang/srt/configs/__init__.py

@@ -5,6 +5,7 @@ from sglang.srt.configs.exaone import ExaoneConfig
 from sglang.srt.configs.janus_pro import MultiModalityConfig
 from sglang.srt.configs.kimi_vl import KimiVLConfig
 from sglang.srt.configs.kimi_vl_moonvit import MoonViTConfig
+from sglang.srt.configs.longcat_flash import LongcatFlashConfig
 from sglang.srt.configs.step3_vl import (
     Step3TextConfig,
     Step3VisionEncoderConfig,
@@ -16,6 +17,7 @@ __all__ = [
     "ChatGLMConfig",
     "DbrxConfig",
     "DeepseekVL2Config",
+    "LongcatFlashConfig",
     "MultiModalityConfig",
     "KimiVLConfig",
     "MoonViTConfig",

python/sglang/srt/configs/longcat_flash.py

+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+FLASH_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class LongcatFlashConfig(PretrainedConfig):
+    model_type = "longcat_flash"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=131072,
+        hidden_size=6144,
+        intermediate_size=None,
+        ffn_hidden_size=12288,
+        expert_ffn_hidden_size=2048,
+        num_layers=28,
+        num_hidden_layers=None,
+        num_attention_heads=64,
+        ep_size=1,
+        kv_lora_rank=512,
+        q_lora_rank=1536,
+        qk_rope_head_dim=128,
+        qk_nope_head_dim=128,
+        v_head_dim=128,
+        n_routed_experts=512,
+        moe_topk=12,
+        norm_topk_prob=False,
+        max_position_embeddings=131072,
+        rms_norm_eps=1e-05,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        mla_scale_q_lora=True,
+        mla_scale_kv_lora=True,
+        torch_dtype="bfloat16",
+        params_dtype="bfloat16",
+        rounter_params_dtype="float32",
+        router_bias=False,
+        topk_method=None,
+        routed_scaling_factor=6.0,
+        zero_expert_num=256,
+        zero_expert_type="identity",
+        nextn_use_scmoe=False,
+        num_nextn_predict_layers=1,
+        **kwargs,
+    ):
+        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,
+            torch_dtype=torch_dtype,
+            params_dtype=params_dtype,
+            rounter_params_dtype=rounter_params_dtype,
+            topk_method=topk_method,
+            router_bias=router_bias,
+            nextn_use_scmoe=nextn_use_scmoe,
+            num_nextn_predict_layers=num_nextn_predict_layers,
+            **kwargs,
+        )
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = (
+            num_hidden_layers if num_hidden_layers is not None else num_layers
+        )
+        self.intermediate_size = (
+            intermediate_size if intermediate_size is not None else ffn_hidden_size
+        )
+        self.moe_intermediate_size = expert_ffn_hidden_size
+        self.num_attention_heads = num_attention_heads
+        self.ep_size = ep_size
+        self.kv_lora_rank = kv_lora_rank
+        self.q_lora_rank = q_lora_rank
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.v_head_dim = v_head_dim
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.n_routed_experts = n_routed_experts
+        self.moe_topk = moe_topk
+        self.norm_topk_prob = norm_topk_prob
+        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.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.mla_scale_q_lora = mla_scale_q_lora
+        self.mla_scale_kv_lora = mla_scale_kv_lora
+        self.zero_expert_num = zero_expert_num
+        self.zero_expert_type = zero_expert_type
+        self.routed_scaling_factor = routed_scaling_factor
+        self.hidden_act = "silu"

python/sglang/srt/configs/model_config.py

@@ -132,6 +132,13 @@ class ModelConfig:
         if is_draft_model and self.hf_config.architectures[0] == "Glm4MoeForCausalLM":
             self.hf_config.architectures[0] = "Glm4MoeForCausalLMNextN"
 
+        if (
+            is_draft_model
+            and self.hf_config.architectures[0] == "LongcatFlashForCausalLM"
+        ):
+            self.hf_config.architectures[0] = "LongcatFlashForCausalLMNextN"
+            self.hf_config.num_hidden_layers = self.hf_config.num_nextn_predict_layers
+
         if is_draft_model and self.hf_config.architectures[0] == "MiMoForCausalLM":
             self.hf_config.architectures[0] = "MiMoMTP"
         if (
@@ -199,6 +206,8 @@ class ModelConfig:
             "DeepseekV2ForCausalLM" in self.hf_config.architectures
             or "DeepseekV3ForCausalLM" in self.hf_config.architectures
             or "DeepseekV3ForCausalLMNextN" in self.hf_config.architectures
+            or "LongcatFlashForCausalLM" in self.hf_config.architectures
+            or "LongcatFlashForCausalLMNextN" in self.hf_config.architectures
         ):
             self.head_dim = 256
             self.attention_arch = AttentionArch.MLA
@@ -270,6 +279,9 @@ class ModelConfig:
             self.num_key_value_heads = self.num_attention_heads
         self.hidden_size = self.hf_text_config.hidden_size
         self.num_hidden_layers = self.hf_text_config.num_hidden_layers
+        self.num_attention_layers = self.num_hidden_layers
+        if "LongcatFlashForCausalLM" in self.hf_config.architectures:
+            self.num_attention_layers = self.num_hidden_layers * 2
         self.num_nextn_predict_layers = getattr(
             self.hf_text_config, "num_nextn_predict_layers", None
         )

python/sglang/srt/hf_transformers_utils.py

@@ -40,6 +40,7 @@ from sglang.srt.configs import (
     DeepseekVL2Config,
     ExaoneConfig,
     KimiVLConfig,
+    LongcatFlashConfig,
     MultiModalityConfig,
     Step3VLConfig,
 )
@@ -56,6 +57,7 @@ _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
     KimiVLConfig.model_type: KimiVLConfig,
     InternVLChatConfig.model_type: InternVLChatConfig,
     Step3VLConfig.model_type: Step3VLConfig,
+    LongcatFlashConfig.model_type: LongcatFlashConfig,
 }
 
 for name, cls in _CONFIG_REGISTRY.items():

python/sglang/srt/layers/moe/ep_moe/kernels.py

@@ -1362,3 +1362,77 @@ def moe_ep_deepgemm_preprocess(
         gateup_input,
         gateup_input_scale,
     )
+
+
+@triton.jit
+def compute_identity_kernel(
+    top_k,
+    hidden_states_ptr,
+    expert_scales_ptr,
+    num_tokens,
+    output_ptr,
+    hidden_dim,
+    scales_stride,
+    BLOCK_SIZE: tl.constexpr,
+):
+    pid = tl.program_id(0)
+
+    batch_id = pid // (hidden_dim // BLOCK_SIZE)
+    dim_offset = pid % (hidden_dim // BLOCK_SIZE) * BLOCK_SIZE
+
+    if batch_id >= num_tokens or dim_offset >= hidden_dim:
+        return
+
+    h = tl.load(
+        hidden_states_ptr
+        + batch_id * hidden_dim
+        + dim_offset
+        + tl.arange(0, BLOCK_SIZE),
+        mask=(dim_offset + tl.arange(0, BLOCK_SIZE)) < hidden_dim,
+    )
+
+    result = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
+    for i in range(top_k):
+        scale = tl.load(expert_scales_ptr + batch_id * scales_stride + i)
+        result += h * scale
+
+    tl.store(
+        output_ptr + batch_id * hidden_dim + dim_offset + tl.arange(0, BLOCK_SIZE),
+        result,
+        mask=(dim_offset + tl.arange(0, BLOCK_SIZE)) < hidden_dim,
+    )
+
+
+def zero_experts_compute_triton(
+    expert_indices, expert_scales, num_experts, zero_expert_type, hidden_states
+):
+    N = expert_indices.numel()
+    top_k = expert_indices.size(-1)
+    grid = lambda meta: (triton.cdiv(N, meta["BLOCK_SIZE"]),)
+
+    if zero_expert_type == "identity":
+        zero_expert_mask = expert_indices < num_experts
+        zero_expert_scales = expert_scales.clone()
+        zero_expert_scales[zero_expert_mask] = 0.0
+
+    normal_expert_mask = expert_indices >= num_experts
+    expert_indices[normal_expert_mask] = 0
+    expert_scales[normal_expert_mask] = 0.0
+
+    output = torch.zeros_like(hidden_states).to(hidden_states.device)
+    hidden_dim = hidden_states.size(-1)
+    num_tokens = hidden_states.size(0)
+
+    grid = lambda meta: (num_tokens * (hidden_dim // meta["BLOCK_SIZE"]),)
+    compute_identity_kernel[grid](
+        top_k,
+        hidden_states,
+        zero_expert_scales,
+        num_tokens,
+        output,
+        hidden_dim,
+        zero_expert_scales.stride(0),
+        BLOCK_SIZE=256,
+    )
+
+    return output

python/sglang/srt/layers/moe/topk.py

@@ -357,7 +357,17 @@ def fused_topk_torch_native(
     gating_output: torch.Tensor,
     topk: int,
     renormalize: bool,
+    correction_bias: torch.Tensor = None,
 ):
+    if correction_bias is not None:
+        n_routed_experts = gating_output.shape[-1]
+        scores = gating_output.softmax(dim=-1)
+        scores_for_choice = scores.view(
+            -1, n_routed_experts
+        ) + correction_bias.unsqueeze(0)
+        topk_ids = torch.topk(scores_for_choice, k=topk, dim=-1, sorted=False)[1]
+        topk_weights = scores.gather(1, topk_ids)
+    else:
         assert (
             hidden_states.shape[0] == gating_output.shape[0]
         ), f"Number of tokens mismatch, {hidden_states.shape=} vs {gating_output.shape=}"
@@ -368,6 +378,7 @@ def fused_topk_torch_native(
         topk_ids = torch.empty(M, topk, dtype=torch.int32, device=hidden_states.device)
         topk_weights = F.softmax(gating_output.float(), dim=-1)
         topk_weights, topk_ids = torch.topk(topk_weights, topk, dim=-1)
+
     if renormalize:
         topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
     return topk_weights, topk_ids
@@ -380,6 +391,7 @@ def fused_topk_cpu(
     renormalize: bool,
     num_token_non_padded: Optional[torch.Tensor] = None,
     expert_location_dispatch_info: Optional[ExpertLocationDispatchInfo] = None,
+    correction_bias: torch.Tensor = None,
 ):
     topk_weights, topk_ids = torch.ops.sgl_kernel.topk_softmax_cpu(
         hidden_states=hidden_states,
@@ -825,6 +837,7 @@ def select_experts(
             gating_output=router_logits,
             topk=top_k,
             renormalize=renormalize,
+            correction_bias=correction_bias,
         )
     elif custom_routing_function is None:
         assert not apply_routed_scaling_factor_on_output, "Not implemented"

python/sglang/srt/layers/quantization/utils.py

@@ -77,6 +77,19 @@ def is_layer_skipped(
                 )
     else:
         is_skipped = prefix in ignored_layers
+        if "gate_up_proj" in prefix:
+            prefix_gate = prefix.replace("gate_up_proj", "gate_proj")
+            prefix_up = prefix.replace("gate_up_proj", "up_proj")
+            if prefix_gate in ignored_layers and prefix_up in ignored_layers:
+                is_skipped = True
+        elif "experts" in prefix:
+            is_skipped = any(
+                [
+                    prefix in layer_name
+                    for layer_name in ignored_layers
+                    if "experts" in layer_name
+                ]
+            )
 
     assert is_skipped is not None
     return is_skipped

python/sglang/srt/model_executor/model_runner.py

@@ -307,7 +307,10 @@ class ModelRunner:
         model_num_layers = (
             self.model_config.num_nextn_predict_layers
             if self.is_draft_worker and model_has_mtp_layers
-            else self.model_config.num_hidden_layers
+            else max(
+                self.model_config.num_hidden_layers,
+                self.model_config.num_attention_layers,
+            )
         )
         self.start_layer = getattr(self.model, "start_layer", 0)
         self.end_layer = getattr(self.model, "end_layer", model_num_layers)

python/sglang/srt/models/longcat_flash.py

+# Apache License, Version 2.0:
+# 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.
+#
+# MIT License:
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import concurrent.futures
+import logging
+import os
+from enum import IntEnum, auto
+from typing import Any, Dict, Iterable, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from tqdm import tqdm
+
+from sglang.srt.configs import LongcatFlashConfig
+from sglang.srt.distributed import (
+    get_tensor_model_parallel_world_size,
+    tensor_model_parallel_all_reduce,
+)
+from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
+from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation
+from sglang.srt.eplb.expert_location_dispatch import ExpertLocationDispatchInfo
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.amx_utils import PackWeightMethod
+from sglang.srt.layers.communicator import LayerCommunicator, LayerScatterModes
+from sglang.srt.layers.dp_attention import (
+    get_attention_tp_rank,
+    get_attention_tp_size,
+    is_dp_attention_enabled,
+)
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import (
+    MergedColumnParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.moe.ep_moe.kernels import zero_experts_compute_triton
+from sglang.srt.layers.moe.ep_moe.layer import DeepEPMoE, get_moe_impl_class
+from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
+from sglang.srt.layers.moe.topk import StandardTopKOutput, TopK
+from sglang.srt.layers.quantization import deep_gemm_wrapper
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.quantization.fp8_kernel import is_fp8_fnuz
+from sglang.srt.layers.quantization.fp8_utils import (
+    block_quant_dequant,
+    block_quant_to_tensor_quant,
+    channel_quant_to_tensor_quant,
+    normalize_e4m3fn_to_e4m3fnuz,
+    requant_weight_ue8m0_inplace,
+)
+from sglang.srt.layers.quantization.int8_utils import (
+    block_dequant as int8_block_dequant,
+)
+from sglang.srt.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.managers.schedule_batch import global_server_args_dict
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.deepseek_v2 import DeepseekV2AttentionMLA
+from sglang.srt.utils import (
+    BumpAllocator,
+    LazyValue,
+    add_prefix,
+    bind_or_assign,
+    cpu_has_amx_support,
+    get_bool_env_var,
+    get_device_sm,
+    get_int_env_var,
+    is_cpu,
+    is_cuda,
+    is_flashinfer_available,
+    is_hip,
+    is_non_idle_and_non_empty,
+    is_npu,
+    is_sm100_supported,
+)
+
+_is_hip = is_hip()
+_is_cuda = is_cuda()
+_is_npu = is_npu()
+_is_fp8_fnuz = is_fp8_fnuz()
+_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
+_is_cpu_amx_available = cpu_has_amx_support()
+_is_cpu = is_cpu()
+_device_sm = get_device_sm()
+
+if _is_cuda:
+    from sgl_kernel import (
+        awq_dequantize,
+        bmm_fp8,
+        dsv3_fused_a_gemm,
+        dsv3_router_gemm,
+        merge_state_v2,
+    )
+elif _is_cpu and _is_cpu_amx_available:
+    pass
+elif _is_hip:
+    from sglang.srt.layers.quantization.awq_triton import (
+        awq_dequantize_triton as awq_dequantize,
+    )
+else:
+    from vllm._custom_ops import awq_dequantize
+
+logger = logging.getLogger(__name__)
+
+
+class LongcatFlashMLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        reduce_results: bool = False,
+        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,
+            reduce_results=reduce_results,
+            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,
+    ):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class LongcatFlashRouter(nn.Module):
+    def __init__(
+        self,
+        config,
+        zero_expert_num=0,
+        rounter_params_dtype=torch.float32,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.n_routed_experts = config.n_routed_experts
+        self.n_routed_experts = self.n_routed_experts + zero_expert_num
+        self.rounter_params_dtype = rounter_params_dtype
+        self.classifier = ReplicatedLinear(
+            config.hidden_size,
+            self.n_routed_experts,
+            bias=config.router_bias,
+            params_dtype=rounter_params_dtype,
+            quant_config=None,
+            prefix=add_prefix("classifier", prefix),
+        )
+        self.e_score_correction_bias = nn.Parameter(
+            torch.zeros((self.n_routed_experts), dtype=rounter_params_dtype)
+        )
+
+    def forward(self, hidden_states):
+        logits, _ = self.classifier(hidden_states.to(self.rounter_params_dtype))
+        return logits
+
+
+class LongcatFlashMoE(nn.Module):
+
+    def __init__(
+        self,
+        config: LongcatFlashConfig,
+        layer_id: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.layer_id = layer_id
+        self.routed_scaling_factor = config.routed_scaling_factor
+        self.num_experts = config.n_routed_experts
+        self.top_k = config.moe_topk
+        self.zero_expert_num = config.zero_expert_num
+        self.zero_expert_type = config.zero_expert_type
+
+        if config.rounter_params_dtype == "float32":
+            self.rounter_params_dtype = torch.float32
+        else:
+            self.rounter_params_dtype = torch.bfloat16
+
+        self.tp_size = get_tensor_model_parallel_world_size()
+
+        if self.tp_size > config.n_routed_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.n_routed_experts}."
+            )
+
+        if config.hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {config.hidden_act}. "
+                "Only silu is supported for now."
+            )
+
+        self.router = LongcatFlashRouter(
+            config=self.config,
+            zero_expert_num=self.zero_expert_num,
+            rounter_params_dtype=self.rounter_params_dtype,
+            prefix=add_prefix("router", prefix),
+        )
+
+        self.topk = TopK(
+            top_k=self.top_k,
+            renormalize=False,
+            use_grouped_topk=False,
+            correction_bias=self.router.e_score_correction_bias.data,
+        )
+        self.topk.forward = self.topk.forward_native
+
+        self.experts = get_moe_impl_class()(
+            num_experts=self.num_experts,
+            top_k=self.top_k,
+            layer_id=self.layer_id,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            quant_config=quant_config,
+            prefix=add_prefix("experts", prefix),
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        num_tokens, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+
+        # router_logits: (num_tokens, n_experts)
+        router_logits = self.router(hidden_states)
+        topk_weights, topk_idx, _ = self.topk(
+            hidden_states,
+            router_logits,
+        )
+        if self.zero_expert_type is not None:
+            zero_expert_result = zero_experts_compute_triton(
+                expert_indices=topk_idx,
+                expert_scales=topk_weights,
+                num_experts=self.num_experts,
+                zero_expert_type=self.zero_expert_type,
+                hidden_states=hidden_states,
+            )
+        topk_output = StandardTopKOutput(topk_weights, topk_idx, _)
+
+        final_hidden_states = self.experts(hidden_states, topk_output)
+        final_hidden_states *= self.routed_scaling_factor
+
+        if self.zero_expert_type is not None and hidden_states.shape[0] > 0:
+            final_hidden_states += zero_expert_result.to(final_hidden_states.device)
+
+        if self.tp_size > 1:
+            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
+
+        return final_hidden_states.view(num_tokens, hidden_dim)
+
+    def get_moe_weights(self):
+        return [
+            x.data
+            for name, x in self.experts.named_parameters()
+            if name not in ["correction_bias"]
+        ]
+
+
+class LongcatFlashDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: LongcatFlashConfig,
+        layer_id: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        alt_stream: Optional[torch.cuda.Stream] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.layer_id = layer_id
+        self.alt_stream = alt_stream
+        self.self_attn = nn.ModuleList(
+            [
+                DeepseekV2AttentionMLA(
+                    config=config,
+                    hidden_size=config.hidden_size,
+                    num_heads=config.num_attention_heads,
+                    qk_nope_head_dim=config.qk_nope_head_dim,
+                    qk_rope_head_dim=config.qk_rope_head_dim,
+                    v_head_dim=config.v_head_dim,
+                    q_lora_rank=config.q_lora_rank,
+                    kv_lora_rank=config.kv_lora_rank,
+                    rope_theta=config.rope_theta,
+                    rope_scaling=None,
+                    max_position_embeddings=config.max_position_embeddings,
+                    quant_config=(
+                        None
+                        if "self_attn" in getattr(config, "disable_quant_module", [])
+                        else quant_config
+                    ),
+                    layer_id=layer_id * 2 + i,
+                    reduce_results=False,
+                    prefix=add_prefix(f"self_attn.{i}", prefix),
+                    alt_stream=self.alt_stream,
+                )
+                for i in range(2)
+            ]
+        )
+
+        self.input_layernorm = nn.ModuleList(
+            [RMSNorm(config.hidden_size, eps=config.rms_norm_eps) for i in range(2)]
+        )
+        self.post_attention_layernorm = nn.ModuleList(
+            [RMSNorm(config.hidden_size, eps=config.rms_norm_eps) for i in range(2)]
+        )
+
+        self.mlps = nn.ModuleList(
+            [
+                LongcatFlashMLP(
+                    hidden_size=config.hidden_size,
+                    intermediate_size=config.intermediate_size,
+                    hidden_act=config.hidden_act,
+                    quant_config=(
+                        None
+                        if "mlps" in getattr(config, "disable_quant_module", [])
+                        else quant_config
+                    ),
+                    prefix=add_prefix(f"mlps.{i}", prefix),
+                )
+                for i in range(2)
+            ]
+        )
+
+        self.mlp = LongcatFlashMoE(
+            layer_id=self.layer_id,
+            config=config,
+            quant_config=quant_config,
+            prefix=add_prefix("mlp", prefix),
+        )
+
+        self.attn_tp_size = get_attention_tp_size()
+        self.attn_tp_rank = get_attention_tp_rank()
+
+        self.mlp_layer_scatter_modes = [
+            LayerScatterModes.init_new(
+                layer_id=self.layer_id * 2 + i,
+                num_layers=config.num_hidden_layers,
+                is_layer_sparse=False,
+                is_previous_layer_sparse=False,
+            )
+            for i in range(2)
+        ]
+        self.mlp_layer_communicator = [
+            LayerCommunicator(
+                layer_scatter_modes=self.mlp_layer_scatter_modes[i],
+                input_layernorm=self.input_layernorm[i],
+                post_attention_layernorm=self.post_attention_layernorm[i],
+            )
+            for i in range(2)
+        ]
+
+        self.moe_layer_scatter_modes = LayerScatterModes.init_new(
+            layer_id=self.layer_id,
+            num_layers=config.num_hidden_layers,
+            is_layer_sparse=True,
+            is_previous_layer_sparse=True,
+        )
+        self.moe_layer_communicator = LayerCommunicator(
+            layer_scatter_modes=self.moe_layer_scatter_modes,
+            input_layernorm=self.input_layernorm[0],
+            post_attention_layernorm=self.post_attention_layernorm[0],
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+        residual: Optional[torch.Tensor],
+        zero_allocator: BumpAllocator,
+    ) -> torch.Tensor:
+        # first_attn
+        hidden_states, residual = self.moe_layer_communicator.prepare_attn(
+            hidden_states, residual, forward_batch
+        )
+        if hidden_states.shape[0] != 0:
+            hidden_states = self.self_attn[0](
+                positions=positions,
+                hidden_states=hidden_states,
+                forward_batch=forward_batch,
+                zero_allocator=zero_allocator,
+            )
+
+        # moe
+        hidden_states, residual = self.moe_layer_communicator.prepare_mlp(
+            hidden_states, residual, forward_batch
+        )
+        moe_hidden_states = hidden_states.clone()
+        moe_residual = residual.clone()
+        moe_hidden_states = self.mlp(moe_hidden_states)
+        moe_hidden_states, moe_residual = self.moe_layer_communicator.postprocess_layer(
+            moe_hidden_states, moe_residual, forward_batch
+        )
+
+        hidden_states, residual = self.forward_mlp(
+            hidden_states, positions, residual, forward_batch, zero_allocator
+        )
+
+        hidden_states = moe_hidden_states + hidden_states
+        return hidden_states, residual
+
+    def forward_mlp(
+        self, hidden_states, positions, residual, forward_batch, zero_allocator
+    ):
+        # first_mlp
+        hidden_states = self.mlps[0](hidden_states)
+        # TP all_reduce
+        hidden_states = tensor_model_parallel_all_reduce(hidden_states)
+
+        # second_attn
+        hidden_states, residual = self.mlp_layer_communicator[1].prepare_attn(
+            hidden_states, residual, forward_batch
+        )
+        if hidden_states.shape[0] != 0:
+            hidden_states = self.self_attn[1](
+                positions=positions,
+                hidden_states=hidden_states,
+                forward_batch=forward_batch,
+                zero_allocator=zero_allocator,
+            )
+
+        # second_mlp
+        hidden_states, residual = self.mlp_layer_communicator[1].prepare_mlp(
+            hidden_states, residual, forward_batch
+        )
+        hidden_states = self.mlps[1](hidden_states)
+        # TP all_reduce
+        hidden_states = tensor_model_parallel_all_reduce(hidden_states)
+
+        hidden_states, residual = self.mlp_layer_communicator[1].postprocess_layer(
+            hidden_states, residual, forward_batch
+        )
+
+        return hidden_states, residual
+
+
+class LongcatFlashModel(nn.Module):
+    fall_back_to_pt_during_load = False
+
+    def __init__(
+        self,
+        config: LongcatFlashConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            enable_tp=not is_dp_attention_enabled(),
+        )
+
+        self.alt_stream = torch.cuda.Stream()
+        self.layers = nn.ModuleList(
+            [
+                LongcatFlashDecoderLayer(
+                    config,
+                    layer_id,
+                    quant_config=quant_config,
+                    prefix=add_prefix(f"layers.{layer_id}", prefix),
+                    alt_stream=self.alt_stream,
+                )
+                for layer_id in range(config.num_hidden_layers)
+            ]
+        )
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def get_input_embeddings(self) -> torch.Tensor:
+        return self.embed_tokens
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+    ) -> torch.Tensor:
+        total_num_layers = len(self.layers)
+        device = input_embeds.device if input_embeds is not None else input_ids.device
+        zero_allocator = BumpAllocator(
+            buffer_size=total_num_layers * 2 * (2 if forward_batch.can_run_tbo else 1),
+            dtype=torch.float32,
+            device=device,
+        )
+        if input_embeds is None:
+            hidden_states = self.embed_tokens(input_ids)
+        else:
+            hidden_states = input_embeds
+
+        residual = None
+
+        for i in range(total_num_layers):
+            with get_global_expert_distribution_recorder().with_current_layer(i):
+                layer = self.layers[i]
+                hidden_states, residual = layer(
+                    positions, hidden_states, forward_batch, residual, zero_allocator
+                )
+
+        if hidden_states.shape[0] != 0:
+            if residual is None:
+                hidden_states = self.norm(hidden_states)
+            else:
+                hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class LongcatFlashForCausalLM(nn.Module):
+    # for quark model load
+    packed_modules_mapping = {}
+
+    def __init__(
+        self,
+        config: LongcatFlashConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        # for quark model load
+        # Fuse q_a_proj and kv_a_proj_with_mqa along output dimension when q_lora_rank is not None
+        self.fuse_qkv_a_proj = (
+            hasattr(config, "q_lora_rank") and config.q_lora_rank is not None
+        )
+        if self.fuse_qkv_a_proj:
+            self.packed_modules_mapping["fused_qkv_a_proj_with_mqa"] = [
+                "q_a_proj",
+                "kv_a_proj_with_mqa",
+            ]
+
+        self.config = config
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.quant_config = quant_config
+        self.model = LongcatFlashModel(
+            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=global_server_args_dict["enable_dp_lm_head"],
+        )
+        self.logits_processor = LogitsProcessor(config)
+
+    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,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
+
+        return self.logits_processor(
+            input_ids, hidden_states, self.lm_head, forward_batch
+        )
+
+    def post_load_weights(self, weight_names=None):
+
+        # Perform post-processing after loading weights
+        if weight_names is None:
+            layer_ids = range(self.config.num_hidden_layers)
+        else:
+            layer_ids = set()
+            for name in weight_names:
+                if "kv_b_proj" in name:
+                    layer_id = int(name.split(".")[2])
+                    if layer_id < self.config.num_hidden_layers:
+                        layer_ids.add(layer_id)
+
+        for layer_id in layer_ids:
+            for i in range(2):
+                self_attn = self.model.layers[layer_id].self_attn[i]
+                if hasattr(self_attn.kv_b_proj, "qweight"):
+                    # AWQ compatible
+                    if _is_cuda or _is_hip:
+                        w = awq_dequantize(
+                            self_attn.kv_b_proj.qweight,
+                            self_attn.kv_b_proj.scales,
+                            self_attn.kv_b_proj.qzeros,
+                        ).T
+                    else:
+                        w = awq_dequantize(
+                            self_attn.kv_b_proj.qweight,
+                            self_attn.kv_b_proj.scales,
+                            self_attn.kv_b_proj.qzeros,
+                            0,
+                            0,
+                            0,
+                        ).T
+                else:
+                    w = self_attn.kv_b_proj.weight
+                # NOTE(HandH1998): Since `bmm_fp8` only supports per-tensor scale, we have to requantize `self_attn.kv_b_proj`.
+                # This may affect the accuracy of fp8 model.
+                # Fix deepseek v3 blockwise bmm by using deep_gemm
+                use_deep_gemm_bmm = False
+
+                if w.dtype in (
+                    torch.float8_e4m3fn,
+                    torch.float8_e4m3fnuz,
+                ):
+                    if (
+                        hasattr(self.quant_config, "weight_block_size")
+                        and self.quant_config.weight_block_size is not None
+                    ):
+                        weight_block_size = self.quant_config.weight_block_size
+                        assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
+                        if _is_fp8_fnuz:
+                            weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
+                                weight=w,
+                                weight_scale=self_attn.kv_b_proj.weight_scale_inv,
+                                input_scale=None,
+                            )
+                        else:
+                            weight = w
+                            weight_scale = self_attn.kv_b_proj.weight_scale_inv
+
+                        if (
+                            _is_cuda
+                            and weight_block_size[0] == 128
+                            and weight_block_size[1] == 128
+                        ):
+                            if (
+                                deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM
+                                and not deep_gemm_wrapper.DEEPGEMM_BLACKWELL
+                                and get_bool_env_var("SGL_USE_DEEPGEMM_BMM", "false")
+                            ):
+                                block_scale = weight_scale
+                                use_deep_gemm_bmm = True
+                            else:
+                                w = block_quant_dequant(
+                                    weight,
+                                    weight_scale,
+                                    weight_block_size,
+                                    torch.bfloat16,
+                                )
+                        else:
+                            w, scale = block_quant_to_tensor_quant(
+                                weight, weight_scale, weight_block_size
+                            )
+                            self_attn.w_scale = scale
+                    else:
+                        if _is_fp8_fnuz:
+                            weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
+                                weight=w,
+                                weight_scale=self_attn.kv_b_proj.weight_scale,
+                                input_scale=None,
+                            )
+                        else:
+                            weight = w
+                            weight_scale = self_attn.kv_b_proj.weight_scale
+
+                        w, scale = channel_quant_to_tensor_quant(weight, weight_scale)
+                        self_attn.w_scale = scale
+
+                if w.dtype == torch.int8:
+                    if hasattr(self.quant_config, "weight_block_size"):
+                        # block-wise int8 need it
+                        weight_block_size = self.quant_config.weight_block_size
+                        if weight_block_size is not None:
+                            assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
+                            weight = w
+                            weight_scale = self_attn.kv_b_proj.weight_scale_inv
+                            w = int8_block_dequant(
+                                weight, weight_scale, weight_block_size
+                            ).to(torch.bfloat16)
+                    else:
+                        # channel-wise int8 need it
+                        w = w.to(torch.bfloat16) * self_attn.kv_b_proj.weight_scale.to(
+                            torch.bfloat16
+                        )
+
+                w_kc, w_vc = w.unflatten(
+                    0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
+                ).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
+                if not use_deep_gemm_bmm:
+                    self_attn.w_kc = bind_or_assign(
+                        self_attn.w_kc,
+                        w_kc.transpose(1, 2).contiguous().transpose(1, 2),
+                    )
+                    self_attn.w_vc = bind_or_assign(
+                        self_attn.w_vc, w_vc.contiguous().transpose(1, 2)
+                    )
+                    if (
+                        hasattr(self_attn.kv_b_proj, "weight_scale")
+                        and self_attn.w_scale is None
+                    ):
+                        self_attn.w_scale = bind_or_assign(
+                            self_attn.w_scale, self_attn.kv_b_proj.weight_scale
+                        )
+                        if _is_hip:
+                            self_attn.w_scale *= 2.0
+                    # TODO: remove this after adding FP8 support in bmm cpu kernel
+                    if (
+                        _is_cpu
+                        and _is_cpu_amx_available
+                        and w.dtype == torch.float8_e4m3fn
+                    ):
+                        self_attn.w_kc = (
+                            self_attn.w_kc.to(torch.bfloat16) * self_attn.w_scale
+                        )
+                        self_attn.w_vc = (
+                            self_attn.w_vc.to(torch.bfloat16) * self_attn.w_scale
+                        )
+                else:
+                    num_tiles_k = self_attn.qk_nope_head_dim // weight_block_size[1]
+                    num_tiles_n = self_attn.v_head_dim // weight_block_size[0]
+                    ws_kc, ws_vc = block_scale.unflatten(
+                        0, (-1, (num_tiles_k + num_tiles_n))
+                    ).split([num_tiles_k, num_tiles_n], dim=1)
+                    self_attn.w_scale_k = bind_or_assign(
+                        self_attn.w_scale_k, ws_kc.transpose(1, 2).contiguous()
+                    )
+                    self_attn.w_scale_v = bind_or_assign(
+                        self_attn.w_scale_v, ws_vc.contiguous()
+                    )
+                    self_attn.w_kc = bind_or_assign(
+                        self_attn.w_kc, w_kc.transpose(1, 2).contiguous()
+                    )
+                    self_attn.w_vc = bind_or_assign(self_attn.w_vc, w_vc.contiguous())
+                    self_attn.use_deep_gemm_bmm = True
+
+                if self.config.mla_scale_q_lora:
+                    self_attn.q_a_layernorm.weight.data *= (
+                        self.config.hidden_size / self.config.q_lora_rank
+                    ) ** 0.5
+                if self.config.mla_scale_kv_lora:
+                    self_attn.kv_a_layernorm.weight.data *= (
+                        self.config.hidden_size / self.config.kv_lora_rank
+                    ) ** 0.5
+
+        if (
+            deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM
+            and deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0
+            and hasattr(self.quant_config, "weight_block_size")
+            and self.quant_config.weight_block_size is not None
+        ):
+            self._weight_requant_ue8m0()
+
+    def _weight_requant_ue8m0(self):
+        weight_block_size = self.quant_config.weight_block_size
+
+        for layer_id in range(self.config.num_hidden_layers):
+            layer = self.model.layers[layer_id]
+            for i in range(2):
+                for module in [
+                    layer.self_attn[i].fused_qkv_a_proj_with_mqa,
+                    layer.self_attn[i].q_b_proj,
+                    layer.self_attn[i].kv_b_proj,
+                    layer.self_attn[i].o_proj,
+                ]:
+                    requant_weight_ue8m0_inplace(
+                        module.weight, module.weight_scale_inv, weight_block_size
+                    )
+                mlp = layer.mlps[i]
+                assert isinstance(mlp, LongcatFlashMLP)
+                for module in [
+                    mlp.gate_up_proj,
+                    mlp.down_proj,
+                ]:
+                    requant_weight_ue8m0_inplace(
+                        module.weight, module.weight_scale_inv, weight_block_size
+                    )
+
+        for layer_id in range(self.config.num_hidden_layers):
+            experts = layer.mlp.experts
+            if isinstance(experts, DeepEPMoE):
+                for w in [
+                    experts.w13_weight_fp8,
+                    experts.w2_weight_fp8,
+                ]:
+                    requant_weight_ue8m0_inplace(w[0], w[1], weight_block_size)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        expert_params_mapping = get_moe_impl_class().make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.n_routed_experts,
+        )
+
+        # Fuse q_a_proj and kv_a_proj_with_mqa along output dimension when q_lora_rank is not None
+        fuse_qkv_a_proj = hasattr(self.config, "q_lora_rank") and (
+            self.config.q_lora_rank is not None
+        )
+        cached_a_proj = {} if fuse_qkv_a_proj else None
+
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            futures = []
+            params_dict = dict(self.named_parameters())
+            weight_names = []
+            for name, loaded_weight in weights:
+                if "mtp" in name:
+                    continue
+                weight_names.append(name)
+                if "rotary_emb.inv_freq" in name:
+                    continue
+                for param_name, weight_name, shard_id in stacked_params_mapping:
+                    # Skip non-stacked layers and experts (experts handled below).
+                    if weight_name not in name:
+                        continue
+                    # We have mlp.experts[0].gate_proj in the checkpoint.
+                    # Since we handle the experts below in expert_params_mapping,
+                    # we need to skip here BEFORE we update the name, otherwise
+                    # name will be updated to mlp.experts[0].gate_up_proj, which
+                    # will then be updated below in expert_params_mapping
+                    # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+                    if ("mlp.experts." in name) and name not in params_dict:
+                        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
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    futures.append(
+                        executor.submit(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
+                        futures.append(
+                            executor.submit(
+                                weight_loader,
+                                param,
+                                loaded_weight,
+                                name,
+                                shard_id=shard_id,
+                                expert_id=expert_id,
+                            )
+                        )
+                        break
+                    else:
+                        # Skip loading extra bias for GPTQ models.
+                        if name.endswith(".bias") and name not in params_dict:
+                            continue
+                        if fuse_qkv_a_proj and (
+                            "q_a_proj" in name or "kv_a_proj_with_mqa" in name
+                        ):
+                            cached_a_proj[name] = loaded_weight
+                            q_a_proj_name = (
+                                name
+                                if "q_a_proj" in name
+                                else name.replace("kv_a_proj_with_mqa", "q_a_proj")
+                            )
+                            kv_a_proj_name = (
+                                name
+                                if "kv_a_proj_with_mqa" in name
+                                else name.replace("q_a_proj", "kv_a_proj_with_mqa")
+                            )
+
+                            # When both q_a_proj and kv_a_proj_with_mqa has been cached, load the fused weight to parameter
+                            if (
+                                q_a_proj_name in cached_a_proj
+                                and kv_a_proj_name in cached_a_proj
+                            ):
+                                q_a_proj_weight = cached_a_proj[q_a_proj_name]
+                                kv_a_proj_weight = cached_a_proj[kv_a_proj_name]
+                                cat_dim = 0
+                                if self.quant_config is not None and (
+                                    self.quant_config.get_name() == "awq"
+                                    or self.quant_config.get_name() == "awq_marlin"
+                                    or self.quant_config.get_name() == "moe_wna16"
+                                ):
+                                    cat_dim = 1
+                                fused_weight = torch.cat(
+                                    [q_a_proj_weight, kv_a_proj_weight], dim=cat_dim
+                                )
+                                param_name = (
+                                    name.replace(
+                                        "q_a_proj", "fused_qkv_a_proj_with_mqa"
+                                    )
+                                    if "q_a_proj" in name
+                                    else name.replace(
+                                        "kv_a_proj_with_mqa",
+                                        "fused_qkv_a_proj_with_mqa",
+                                    )
+                                )
+                                param = params_dict[param_name]
+
+                                weight_loader = getattr(
+                                    param, "weight_loader", default_weight_loader
+                                )
+                                futures.append(
+                                    executor.submit(weight_loader, param, fused_weight)
+                                )
+                                cached_a_proj.pop(q_a_proj_name)
+                                cached_a_proj.pop(kv_a_proj_name)
+                        else:
+                            if (
+                                "k_scale" in name or "v_scale" in name
+                            ) and name not in params_dict:
+                                # modelopt attn kv scale is named differently
+                                for scale in ["k_scale", "v_scale"]:
+                                    if scale in name:
+                                        name = name.replace(
+                                            f"{scale[0]}_proj", "attn_mqa"
+                                        )
+                                        break
+                            if name not in params_dict:
+                                # modelopt ckpt contains not needed weights for MTP module:
+                                # model.decoder.self_attn.attn_mqa.v_scale and
+                                # model.decoder.self_attn.attn_mqa.k_scale
+                                logger.warning(f"{name} not found in params_dict.")
+                                continue
+                            param = params_dict[name]
+                            weight_loader = getattr(
+                                param, "weight_loader", default_weight_loader
+                            )
+                            futures.append(
+                                executor.submit(weight_loader, param, loaded_weight)
+                            )
+
+            # Wait for all tasks to complete and raise any exceptions.
+            for future in concurrent.futures.as_completed(futures):
+                future.result()
+
+        self.post_load_weights(weight_names=weight_names)
+
+    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()
+
+    @classmethod
+    def get_model_config_for_expert_location(cls, config):
+        return ModelConfigForExpertLocation(
+            num_layers=config.num_hidden_layers,
+            num_logical_experts=config.n_routed_experts,
+        )
+
+
+EntryClass = [LongcatFlashForCausalLM]

python/sglang/srt/models/longcat_flash_nextn.py

+# Apache License, Version 2.0:
+# 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.
+#
+# MIT License:
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import concurrent.futures
+import logging
+import os
+from enum import IntEnum, auto
+from typing import Any, Dict, Iterable, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from tqdm import tqdm
+
+from sglang.srt.configs import LongcatFlashConfig
+from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
+from sglang.srt.layers.communicator import LayerCommunicator, LayerScatterModes
+from sglang.srt.layers.dp_attention import (
+    get_attention_tp_rank,
+    get_attention_tp_size,
+    is_dp_attention_enabled,
+)
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import ReplicatedLinear
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.quantization import deep_gemm_wrapper
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.quantization.fp8_kernel import is_fp8_fnuz
+from sglang.srt.layers.quantization.fp8_utils import (
+    block_quant_dequant,
+    block_quant_to_tensor_quant,
+    channel_quant_to_tensor_quant,
+    normalize_e4m3fn_to_e4m3fnuz,
+    requant_weight_ue8m0_inplace,
+)
+from sglang.srt.layers.quantization.int8_utils import (
+    block_dequant as int8_block_dequant,
+)
+from sglang.srt.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.deepseek_v2 import DeepseekV2AttentionMLA
+from sglang.srt.models.longcat_flash import LongcatFlashForCausalLM, LongcatFlashMLP
+from sglang.srt.utils import (
+    BumpAllocator,
+    LazyValue,
+    add_prefix,
+    bind_or_assign,
+    cpu_has_amx_support,
+    get_bool_env_var,
+    get_device_sm,
+    is_cpu,
+    is_cuda,
+    is_hip,
+    is_npu,
+)
+
+_is_hip = is_hip()
+_is_cuda = is_cuda()
+_is_npu = is_npu()
+_is_fp8_fnuz = is_fp8_fnuz()
+_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
+_is_cpu_amx_available = cpu_has_amx_support()
+_is_cpu = is_cpu()
+_device_sm = get_device_sm()
+
+if _is_cuda:
+    from sgl_kernel import (
+        awq_dequantize,
+        bmm_fp8,
+        dsv3_fused_a_gemm,
+        dsv3_router_gemm,
+        merge_state_v2,
+    )
+elif _is_cpu and _is_cpu_amx_available:
+    pass
+elif _is_hip:
+    from sglang.srt.layers.quantization.awq_triton import (
+        awq_dequantize_triton as awq_dequantize,
+    )
+else:
+    from vllm._custom_ops import awq_dequantize
+
+
+logger = logging.getLogger(__name__)
+
+
+class LongcatFlashDenseDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: LongcatFlashConfig,
+        layer_id: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        alt_stream: Optional[torch.cuda.Stream] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.layer_id = layer_id
+        self.alt_stream = alt_stream
+
+        self.self_attn = DeepseekV2AttentionMLA(
+            config=config,
+            hidden_size=config.hidden_size,
+            num_heads=config.num_attention_heads,
+            qk_nope_head_dim=config.qk_nope_head_dim,
+            qk_rope_head_dim=config.qk_rope_head_dim,
+            v_head_dim=config.v_head_dim,
+            q_lora_rank=config.q_lora_rank,
+            kv_lora_rank=config.kv_lora_rank,
+            rope_theta=config.rope_theta,
+            rope_scaling=None,
+            max_position_embeddings=config.max_position_embeddings,
+            quant_config=quant_config,
+            layer_id=layer_id,
+            reduce_results=False,
+            prefix=add_prefix(f"self_attn", prefix),
+            alt_stream=self.alt_stream,
+        )
+
+        self.mlp = LongcatFlashMLP(
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            prefix=add_prefix(f"mlps", prefix),
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+        self.attn_tp_size = get_attention_tp_size()
+        self.attn_tp_rank = get_attention_tp_rank()
+        self.layer_scatter_modes = LayerScatterModes.init_new(
+            layer_id=self.layer_id,
+            num_layers=config.num_hidden_layers,
+            is_layer_sparse=False,
+            is_previous_layer_sparse=False,
+        )
+        self.layer_communicator = LayerCommunicator(
+            layer_scatter_modes=self.layer_scatter_modes,
+            input_layernorm=self.input_layernorm,
+            post_attention_layernorm=self.post_attention_layernorm,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+        residual: Optional[torch.Tensor],
+        zero_allocator: BumpAllocator,
+    ) -> torch.Tensor:
+
+        hidden_states, residual = self.layer_communicator.prepare_attn(
+            hidden_states, residual, forward_batch
+        )
+        if hidden_states.shape[0] != 0:
+            hidden_states = self.self_attn(
+                positions=positions,
+                hidden_states=hidden_states,
+                forward_batch=forward_batch,
+                zero_allocator=zero_allocator,
+            )
+
+        hidden_states, residual = self.layer_communicator.prepare_mlp(
+            hidden_states, residual, forward_batch
+        )
+        hidden_states = self.mlp(hidden_states)
+        hidden_states, residual = self.layer_communicator.postprocess_layer(
+            hidden_states, residual, forward_batch
+        )
+        return hidden_states, residual
+
+
+class LongcatFlashModelNextN(nn.Module):
+    def __init__(
+        self,
+        config: LongcatFlashConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.vocab_size = config.vocab_size
+        self.alt_stream = torch.cuda.Stream()
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            enable_tp=not is_dp_attention_enabled(),
+            prefix=add_prefix("embed_tokens", prefix),
+        )
+
+        self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.eh_proj = ReplicatedLinear(
+            2 * config.hidden_size,
+            config.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=add_prefix("eh_proj", ""),
+        )
+        self.decoder = LongcatFlashDenseDecoderLayer(
+            config, 0, quant_config=quant_config, alt_stream=self.alt_stream
+        )
+
+        self.final_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def get_input_embeddings(self) -> torch.Tensor:
+        return self.embed_tokens
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+    ) -> torch.Tensor:
+        total_num_layers = 1
+        device = input_embeds.device if input_embeds is not None else input_ids.device
+        zero_allocator = BumpAllocator(
+            buffer_size=total_num_layers * 2 * (2 if forward_batch.can_run_tbo else 1),
+            dtype=torch.float32,
+            device=device,
+        )
+        if input_embeds is None:
+            hidden_states = self.embed_tokens(input_ids)
+        else:
+            hidden_states = input_embeds
+
+        if hidden_states.shape[0] > 0:
+            hidden_states, _ = self.eh_proj(
+                torch.cat(
+                    (
+                        self.enorm(hidden_states),
+                        self.hnorm(forward_batch.spec_info.hidden_states),
+                    ),
+                    dim=-1,
+                )
+            )
+
+        residual = None
+        with get_global_expert_distribution_recorder().disable_this_region():
+            hidden_states, residual = self.decoder(
+                positions, hidden_states, forward_batch, residual, zero_allocator
+            )
+
+        if not forward_batch.forward_mode.is_idle():
+            if residual is not None:
+                hidden_states, _ = self.final_layernorm(hidden_states, residual)
+            else:
+                hidden_states = self.final_layernorm(hidden_states)
+        return hidden_states
+
+
+class LongcatFlashForCausalLMNextN(LongcatFlashForCausalLM):
+
+    def __init__(
+        self,
+        config: LongcatFlashConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        nn.Module.__init__(self)
+        self.config = config
+        self.quant_config = (
+            None
+            if "mtp" in getattr(config, "disable_quant_module", [])
+            else quant_config
+        )
+        self.model = LongcatFlashModelNextN(config, self.quant_config)
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.hidden_size,
+            quant_config=self.quant_config,
+        )
+        self.logits_processor = LogitsProcessor(config)
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, forward_batch)
+        return self.logits_processor(
+            input_ids, hidden_states, self.lm_head, forward_batch
+        )
+
+    def post_load_weights(self):
+        self_attn = self.model.decoder.self_attn
+        if hasattr(self_attn.kv_b_proj, "qweight"):
+            # AWQ compatible
+            if _is_cuda or _is_hip:
+                w = awq_dequantize(
+                    self_attn.kv_b_proj.qweight,
+                    self_attn.kv_b_proj.scales,
+                    self_attn.kv_b_proj.qzeros,
+                ).T
+            else:
+                w = awq_dequantize(
+                    self_attn.kv_b_proj.qweight,
+                    self_attn.kv_b_proj.scales,
+                    self_attn.kv_b_proj.qzeros,
+                    0,
+                    0,
+                    0,
+                ).T
+        else:
+            w = self_attn.kv_b_proj.weight
+        # NOTE(HandH1998): Since `bmm_fp8` only supports per-tensor scale, we have to requantize `self_attn.kv_b_proj`.
+        # This may affect the accuracy of fp8 model.
+        # Fix deepseek v3 blockwise bmm by using deep_gemm
+        use_deep_gemm_bmm = False
+        if w.dtype in (
+            torch.float8_e4m3fn,
+            torch.float8_e4m3fnuz,
+        ):
+            if (
+                hasattr(self.quant_config, "weight_block_size")
+                and self.quant_config.weight_block_size is not None
+            ):
+                weight_block_size = self.quant_config.weight_block_size
+                assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
+                if _is_fp8_fnuz:
+                    weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
+                        weight=w,
+                        weight_scale=self_attn.kv_b_proj.weight_scale_inv,
+                        input_scale=None,
+                    )
+                else:
+                    weight = w
+                    weight_scale = self_attn.kv_b_proj.weight_scale_inv
+                if (
+                    _is_cuda
+                    and weight_block_size[0] == 128
+                    and weight_block_size[1] == 128
+                ):
+                    if (
+                        deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM
+                        and not deep_gemm_wrapper.DEEPGEMM_BLACKWELL
+                        and get_bool_env_var("SGL_USE_DEEPGEMM_BMM", "false")
+                    ):
+                        block_scale = weight_scale
+                        use_deep_gemm_bmm = True
+                    else:
+                        w = block_quant_dequant(
+                            weight,
+                            weight_scale,
+                            weight_block_size,
+                            torch.bfloat16,
+                        )
+                else:
+                    w, scale = block_quant_to_tensor_quant(
+                        weight, weight_scale, weight_block_size
+                    )
+                    self_attn.w_scale = scale
+            else:
+                if _is_fp8_fnuz:
+                    weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
+                        weight=w,
+                        weight_scale=self_attn.kv_b_proj.weight_scale,
+                        input_scale=None,
+                    )
+                else:
+                    weight = w
+                    weight_scale = self_attn.kv_b_proj.weight_scale
+                w, scale = channel_quant_to_tensor_quant(weight, weight_scale)
+                self_attn.w_scale = scale
+        if w.dtype == torch.int8:
+            if hasattr(self.quant_config, "weight_block_size"):
+                # block-wise int8 need it
+                weight_block_size = self.quant_config.weight_block_size
+                if weight_block_size is not None:
+                    assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
+                    weight = w
+                    weight_scale = self_attn.kv_b_proj.weight_scale_inv
+                    w = int8_block_dequant(weight, weight_scale, weight_block_size).to(
+                        torch.bfloat16
+                    )
+            else:
+                # channel-wise int8 need it
+                w = w.to(torch.bfloat16) * self_attn.kv_b_proj.weight_scale.to(
+                    torch.bfloat16
+                )
+        w_kc, w_vc = w.unflatten(
+            0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
+        ).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
+        if not use_deep_gemm_bmm:
+            self_attn.w_kc = bind_or_assign(
+                self_attn.w_kc, w_kc.transpose(1, 2).contiguous().transpose(1, 2)
+            )
+            self_attn.w_vc = bind_or_assign(
+                self_attn.w_vc, w_vc.contiguous().transpose(1, 2)
+            )
+            if (
+                hasattr(self_attn.kv_b_proj, "weight_scale")
+                and self_attn.w_scale is None
+            ):
+                self_attn.w_scale = bind_or_assign(
+                    self_attn.w_scale, self_attn.kv_b_proj.weight_scale
+                )
+                if _is_hip:
+                    self_attn.w_scale *= 2.0
+            # TODO: remove this after adding FP8 support in bmm cpu kernel
+            if _is_cpu and _is_cpu_amx_available and w.dtype == torch.float8_e4m3fn:
+                self_attn.w_kc = self_attn.w_kc.to(torch.bfloat16) * self_attn.w_scale
+                self_attn.w_vc = self_attn.w_vc.to(torch.bfloat16) * self_attn.w_scale
+        else:
+            num_tiles_k = self_attn.qk_nope_head_dim // weight_block_size[1]
+            num_tiles_n = self_attn.v_head_dim // weight_block_size[0]
+            ws_kc, ws_vc = block_scale.unflatten(
+                0, (-1, (num_tiles_k + num_tiles_n))
+            ).split([num_tiles_k, num_tiles_n], dim=1)
+            self_attn.w_scale_k = bind_or_assign(
+                self_attn.w_scale_k, ws_kc.transpose(1, 2).contiguous()
+            )
+            self_attn.w_scale_v = bind_or_assign(
+                self_attn.w_scale_v, ws_vc.contiguous()
+            )
+            self_attn.w_kc = bind_or_assign(
+                self_attn.w_kc, w_kc.transpose(1, 2).contiguous()
+            )
+            self_attn.w_vc = bind_or_assign(self_attn.w_vc, w_vc.contiguous())
+            self_attn.use_deep_gemm_bmm = True
+
+        if self.config.mla_scale_q_lora:
+            self_attn.q_a_layernorm.weight.data *= (
+                self.config.hidden_size / self.config.q_lora_rank
+            ) ** 0.5
+        if self.config.mla_scale_kv_lora:
+            self_attn.kv_a_layernorm.weight.data *= (
+                self.config.hidden_size / self.config.kv_lora_rank
+            ) ** 0.5
+
+        if (
+            deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM
+            and deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0
+            and hasattr(self.quant_config, "weight_block_size")
+            and self.quant_config.weight_block_size is not None
+        ):
+            self._weight_requant_ue8m0()
+
+    def _weight_requant_ue8m0(self):
+        weight_block_size = self.quant_config.weight_block_size
+        layer = self.model.decoder
+        for module in [
+            layer.self_attn.fused_qkv_a_proj_with_mqa,
+            layer.self_attn.q_b_proj,
+            layer.self_attn.kv_b_proj,
+            layer.self_attn.o_proj,
+        ]:
+            requant_weight_ue8m0_inplace(
+                module.weight, module.weight_scale_inv, weight_block_size
+            )
+        mlp = layer.mlps
+        assert isinstance(mlp, LongcatFlashMLP)
+        for module in [
+            mlp.gate_up_proj,
+            mlp.down_proj,
+        ]:
+            requant_weight_ue8m0_inplace(
+                module.weight, module.weight_scale_inv, weight_block_size
+            )
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        # Fuse q_a_proj and kv_a_proj_with_mqa along output dimension when q_lora_rank is not None
+        fuse_qkv_a_proj = hasattr(self.config, "q_lora_rank") and (
+            self.config.q_lora_rank is not None
+        )
+        cached_a_proj = {} if fuse_qkv_a_proj else None
+
+        nextn_layer_prefix = "model.layers.0"
+        nextn_spec_weight_names = [
+            "shared_head.norm",
+            "eh_proj",
+            "enorm",
+            "hnorm",
+            "final_layernorm",
+        ]
+
+        weight_names_mapping = {
+            "model.mtp.embed_tokens.weight": "embed_tokens.weight",
+            "model.mtp.layers.0.eh_proj.weight": "eh_proj.weight",
+            "model.mtp.layers.0.eh_proj.weight_scale_inv": "eh_proj.weight_scale_inv",
+            "model.mtp.layers.0.enorm.m.weight": "enorm.weight",
+            "model.mtp.layers.0.hnorm.m.weight": "hnorm.weight",
+            "model.mtp.layers.0.input_layernorm.weight": "layers.0.input_layernorm.weight",
+            "model.mtp.layers.0.post_attention_layernorm.weight": "layers.0.post_attention_layernorm.weight",
+            "model.mtp.layers.0.self_attn.kv_a_layernorm.weight": "layers.0.self_attn.kv_a_layernorm.weight",
+            "model.mtp.layers.0.self_attn.kv_a_proj_with_mqa.weight": "layers.0.self_attn.kv_a_proj_with_mqa.weight",
+            "model.mtp.layers.0.self_attn.kv_a_proj_with_mqa.weight_scale_inv": "layers.0.self_attn.kv_a_proj_with_mqa.weight_scale_inv",
+            "model.mtp.layers.0.self_attn.kv_b_proj.weight": "layers.0.self_attn.kv_b_proj.weight",
+            "model.mtp.layers.0.self_attn.kv_b_proj.weight_scale_inv": "layers.0.self_attn.kv_b_proj.weight_scale_inv",
+            "model.mtp.layers.0.self_attn.o_proj.weight": "layers.0.self_attn.o_proj.weight",
+            "model.mtp.layers.0.self_attn.o_proj.weight_scale_inv": "layers.0.self_attn.o_proj.weight_scale_inv",
+            "model.mtp.layers.0.self_attn.q_a_layernorm.weight": "layers.0.self_attn.q_a_layernorm.weight",
+            "model.mtp.layers.0.self_attn.q_a_proj.weight": "layers.0.self_attn.q_a_proj.weight",
+            "model.mtp.layers.0.self_attn.q_a_proj.weight_scale_inv": "layers.0.self_attn.q_a_proj.weight_scale_inv",
+            "model.mtp.layers.0.self_attn.q_b_proj.weight": "layers.0.self_attn.q_b_proj.weight",
+            "model.mtp.layers.0.self_attn.q_b_proj.weight_scale_inv": "layers.0.self_attn.q_b_proj.weight_scale_inv",
+            "model.mtp.layers.0.transformer_layer.mlp.down_proj.weight": "layers.0.mlp.down_proj.weight",
+            "model.mtp.layers.0.transformer_layer.mlp.down_proj.weight_scale_inv": "layers.0.mlp.down_proj.weight_scale_inv",
+            "model.mtp.layers.0.transformer_layer.mlp.gate_proj.weight": "layers.0.mlp.gate_proj.weight",
+            "model.mtp.layers.0.transformer_layer.mlp.gate_proj.weight_scale_inv": "layers.0.mlp.gate_proj.weight_scale_inv",
+            "model.mtp.layers.0.transformer_layer.mlp.up_proj.weight": "layers.0.mlp.up_proj.weight",
+            "model.mtp.layers.0.transformer_layer.mlp.up_proj.weight_scale_inv": "layers.0.mlp.up_proj.weight_scale_inv",
+            "model.mtp.norm.weight": "layers.0.final_layernorm.weight",
+        }
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            futures = []
+            params_dict = dict(self.named_parameters())
+            weight_names = []
+            for name, loaded_weight in weights:
+                if ".mtp." not in name:
+                    continue
+                if name in weight_names_mapping:
+                    name = weight_names_mapping[name]
+                if name.startswith("layers.0"):
+                    name = "model." + name
+                if (
+                    name.startswith("enorm")
+                    or name.startswith("hnorm")
+                    or name.startswith("eh_proj")
+                ):
+                    name = nextn_layer_prefix + "." + name
+                if not name.startswith(nextn_layer_prefix):
+                    continue
+
+                # Use shared head and embed weights from target model
+                if "shared_head.head" in name or "embed_tokens" in name:
+                    continue
+
+                is_decoder = True
+                # For nextn specific weights
+                for weight_name in nextn_spec_weight_names:
+                    if weight_name in name:
+                        name = name.replace(nextn_layer_prefix, "model")
+                        is_decoder = False
+                        break
+                # For decoder layer weights
+                if is_decoder:
+                    name = name.replace(nextn_layer_prefix, "model.decoder")
+
+                weight_names.append(name)
+                if "rotary_emb.inv_freq" in name:
+                    continue
+                for param_name, weight_name, shard_id in stacked_params_mapping:
+                    # Skip non-stacked layers and experts (experts handled below).
+                    if weight_name not in name:
+                        continue
+                    # We have mlp.experts[0].gate_proj in the checkpoint.
+                    # Since we handle the experts below in expert_params_mapping,
+                    # we need to skip here BEFORE we update the name, otherwise
+                    # name will be updated to mlp.experts[0].gate_up_proj, which
+                    # will then be updated below in expert_params_mapping
+                    # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+                    if ("mlp.experts." in name) and name not in params_dict:
+                        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
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    futures.append(
+                        executor.submit(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 fuse_qkv_a_proj and (
+                        "q_a_proj" in name or "kv_a_proj_with_mqa" in name
+                    ):
+                        cached_a_proj[name] = loaded_weight
+                        q_a_proj_name = (
+                            name
+                            if "q_a_proj" in name
+                            else name.replace("kv_a_proj_with_mqa", "q_a_proj")
+                        )
+                        kv_a_proj_name = (
+                            name
+                            if "kv_a_proj_with_mqa" in name
+                            else name.replace("q_a_proj", "kv_a_proj_with_mqa")
+                        )
+
+                        # When both q_a_proj and kv_a_proj_with_mqa has been cached, load the fused weight to parameter
+                        if (
+                            q_a_proj_name in cached_a_proj
+                            and kv_a_proj_name in cached_a_proj
+                        ):
+                            q_a_proj_weight = cached_a_proj[q_a_proj_name]
+                            kv_a_proj_weight = cached_a_proj[kv_a_proj_name]
+                            cat_dim = 0
+                            if self.quant_config is not None and (
+                                self.quant_config.get_name() == "awq"
+                                or self.quant_config.get_name() == "awq_marlin"
+                                or self.quant_config.get_name() == "moe_wna16"
+                            ):
+                                cat_dim = 1
+                            fused_weight = torch.cat(
+                                [q_a_proj_weight, kv_a_proj_weight], dim=cat_dim
+                            )
+                            param_name = (
+                                name.replace("q_a_proj", "fused_qkv_a_proj_with_mqa")
+                                if "q_a_proj" in name
+                                else name.replace(
+                                    "kv_a_proj_with_mqa",
+                                    "fused_qkv_a_proj_with_mqa",
+                                )
+                            )
+                            param = params_dict[param_name]
+
+                            weight_loader = getattr(
+                                param, "weight_loader", default_weight_loader
+                            )
+                            futures.append(
+                                executor.submit(weight_loader, param, fused_weight)
+                            )
+                            cached_a_proj.pop(q_a_proj_name)
+                            cached_a_proj.pop(kv_a_proj_name)
+                    else:
+                        if (
+                            "k_scale" in name or "v_scale" in name
+                        ) and name not in params_dict:
+                            # modelopt attn kv scale is named differently
+                            for scale in ["k_scale", "v_scale"]:
+                                if scale in name:
+                                    name = name.replace(f"{scale[0]}_proj", "attn_mqa")
+                                    break
+                        if name not in params_dict:
+                            # modelopt ckpt contains not needed weights for MTP module:
+                            # model.decoder.self_attn.attn_mqa.v_scale and
+                            # model.decoder.self_attn.attn_mqa.k_scale
+                            logger.warning(f"{name} not found in params_dict.")
+                            continue
+                        param = params_dict[name]
+                        weight_loader = getattr(
+                            param, "weight_loader", default_weight_loader
+                        )
+                        futures.append(
+                            executor.submit(weight_loader, param, loaded_weight)
+                        )
+        self.post_load_weights()
+
+
+EntryClass = [LongcatFlashForCausalLMNextN]