[feature] Initial block diffusion language model support (#12588)

Co-authored-by: Tiwei Bie <tiwei.btw@antgroup.com>

python/sglang/srt/dllm/algorithm/__init__.py

@@ -0,0 +1,39 @@
+import importlib
+import logging
+import pkgutil
+
+from sglang.srt.dllm.config import DllmConfig
+
+logger = logging.getLogger(__name__)
+
+
+def import_algorithms():
+    mapping = {}
+    package_name = "sglang.srt.dllm.algorithm"
+    package = importlib.import_module(package_name)
+    for _, name, ispkg in pkgutil.iter_modules(package.__path__, package_name + "."):
+        if ispkg:
+            continue
+        try:
+            module = importlib.import_module(name)
+        except Exception as e:
+            logger.warning(f"Ignore import error when loading {name}: {e}")
+            continue
+        if not hasattr(module, "Algorithm"):
+            continue
+
+        algo = module.Algorithm
+        mapping[algo.__name__] = algo
+
+    return mapping
+
+
+def get_algorithm(config: DllmConfig):
+    try:
+        name = config.algorithm
+        return algo_name_to_cls[name](config)
+    except:
+        raise RuntimeError(f"Unknown diffusion LLM algorithm: {name}")
+
+
+algo_name_to_cls = import_algorithms()

python/sglang/srt/dllm/algorithm/base.py

@@ -0,0 +1,18 @@
+from sglang.srt.dllm.algorithm import get_algorithm
+from sglang.srt.dllm.config import DllmConfig
+from sglang.srt.server_args import ServerArgs
+
+
+class DllmAlgorithm:
+
+    def __init__(
+        self,
+        config: DllmConfig,
+    ):
+        self.block_size = config.block_size
+        self.mask_id = config.mask_id
+
+    @staticmethod
+    def from_server_args(server_args: ServerArgs):
+        config = DllmConfig.from_server_args(server_args)
+        return get_algorithm(config)

python/sglang/srt/dllm/algorithm/low_confidence.py

@@ -0,0 +1,59 @@
+from typing import Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from sglang.srt.dllm.algorithm.base import DllmAlgorithm
+from sglang.srt.layers.logits_processor import LogitsProcessorOutput
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_executor.model_runner import ModelRunner
+
+
+class LowConfidence(DllmAlgorithm):
+
+    def run(
+        self,
+        model_runner: ModelRunner,
+        forward_batch: ForwardBatch,
+    ) -> Tuple[
+        Union[LogitsProcessorOutput, torch.Tensor], Optional[torch.Tensor], bool
+    ]:
+        mask_index = forward_batch.input_ids == self.mask_id
+        start = len(forward_batch.input_ids) - torch.sum(mask_index).item()
+
+        for _ in range(self.block_size):
+            mask_index = forward_batch.input_ids == self.mask_id
+            if torch.sum(mask_index).item() == 0:
+                break
+
+            logits_output, can_run_cuda_graph = model_runner.forward(
+                forward_batch, pp_proxy_tensors=None
+            )
+
+            x = torch.argmax(logits_output.full_logits, dim=-1)
+            p = torch.squeeze(
+                torch.gather(
+                    F.softmax(logits_output.full_logits, dim=-1),
+                    dim=-1,
+                    index=torch.unsqueeze(x, -1),
+                ),
+                -1,
+            )
+            x = torch.where(mask_index, x, forward_batch.input_ids)
+            confidence = torch.where(mask_index, p, -np.inf)
+            transfer_index = torch.zeros_like(x, dtype=torch.bool, device=x.device)
+            _, select_index = torch.topk(confidence, k=1)
+            transfer_index[select_index] = True
+
+            forward_batch.input_ids[transfer_index] = x[transfer_index]
+
+        logits_output, can_run_cuda_graph = model_runner.forward(
+            forward_batch, pp_proxy_tensors=None
+        )
+
+        next_token_ids = forward_batch.input_ids[start:]
+        return logits_output, next_token_ids, can_run_cuda_graph
+
+
+Algorithm = LowConfidence

python/sglang/srt/dllm/config.py

@@ -0,0 +1,40 @@
+from sglang.srt.configs.model_config import ModelConfig
+from sglang.srt.server_args import ServerArgs
+
+
+class DllmConfig:
+    def __init__(
+        self,
+        mask_id: int,
+        block_size: int,
+        algorithm: str,
+    ):
+        self.algorithm = algorithm
+        self.block_size = block_size
+        self.mask_id = mask_id
+
+    @staticmethod
+    def from_server_args(
+        server_args: ServerArgs,
+    ):
+        if server_args.dllm_algorithm is None:
+            return None
+
+        config = ModelConfig.from_server_args(
+            server_args,
+            model_path=server_args.model_path,
+            model_revision=server_args.revision,
+        )
+
+        if config.hf_config.architectures[0] == "LLaDA2MoeModelLM":
+            mask_id = 156895
+        else:
+            raise RuntimeError(
+                f"Unknown diffusion LLM: {config.hf_config.architectures[0]}"
+            )
+
+        return DllmConfig(
+            algorithm=server_args.dllm_algorithm,
+            block_size=server_args.dllm_block_size,
+            mask_id=mask_id,
+        )

python/sglang/srt/layers/attention/flashinfer_backend.py

@@ -126,6 +126,8 @@ class FlashInferAttnBackend(AttentionBackend):
             model_runner.server_args.multi_item_scoring_delimiter
         )
 
+        self.is_dllm_model = model_runner.server_args.dllm_algorithm is not None
+
         # Parse constants
         self.decode_use_tensor_cores = should_use_tensor_core(
             kv_cache_dtype=model_runner.kv_cache_dtype,
@@ -766,11 +768,16 @@ class FlashInferAttnBackend(AttentionBackend):
                 )
 
             else:
+                if not self.is_dllm_model:
+                    # TODO: design a better interface
+                    # For other models, use causal attention for the ragged part as previously
+                    causal = True
+
                 o1, s1 = self.prefill_wrapper_ragged.forward_return_lse(
                     q.view(-1, layer.tp_q_head_num, layer.head_dim),
                     k.view(-1, layer.tp_k_head_num, layer.head_dim),
                     v.view(-1, layer.tp_v_head_num, layer.head_dim),
-                    causal=True,
+                    causal=causal,
                     sm_scale=layer.scaling,
                     logits_soft_cap=logits_soft_cap,
                 )

python/sglang/srt/layers/logits_processor.py

@@ -99,6 +99,9 @@ class LogitsProcessorOutput:
     )
     input_token_ids_logprobs_idx: Optional[List] = None
 
+    ## Part 4: Diffusion LLM only.
+    full_logits: Optional[torch.Tensor] = None
+
 
 @dataclasses.dataclass
 class LogitsMetadata:
@@ -229,7 +232,11 @@ class LogitsMetadata:
 
 class LogitsProcessor(nn.Module):
     def __init__(
-        self, config, skip_all_gather: bool = False, logit_scale: Optional[float] = None
+        self,
+        config,
+        skip_all_gather: bool = False,
+        logit_scale: Optional[float] = None,
+        return_full_logits: bool = False,
     ):
         super().__init__()
         self.config = config
@@ -258,6 +265,8 @@ class LogitsProcessor(nn.Module):
         ):
             self.final_logit_softcapping = None
 
+        self.return_full_logits = return_full_logits
+
         # enable chunked logprobs processing
         self.enable_logprobs_chunk = envs.SGLANG_ENABLE_LOGITS_PROCESSER_CHUNK.value
         # chunk size for logprobs processing
@@ -491,6 +500,12 @@ class LogitsProcessor(nn.Module):
                 input_logprob_indices, device=pruned_states.device, dtype=torch.int64
             )
 
+        full_logits = (
+            self._get_logits(hidden_states, lm_head, logits_metadata)
+            if self.return_full_logits
+            else None
+        )
+
         hidden_states_to_store: Optional[torch.Tensor] = None
         if logits_metadata.capture_hidden_mode.need_capture():
             if logits_metadata.capture_hidden_mode.is_full():
@@ -529,6 +544,7 @@ class LogitsProcessor(nn.Module):
 
             # Decode mode or extend mode without return_logprob.
             return LogitsProcessorOutput(
+                full_logits=full_logits,
                 next_token_logits=sampled_logits,
                 hidden_states=hidden_states_to_store,
             )
@@ -585,6 +601,7 @@ class LogitsProcessor(nn.Module):
             )
 
         return LogitsProcessorOutput(
+            full_logits=full_logits,
             next_token_logits=sampled_logits,
             hidden_states=hidden_states_to_store,
             input_token_logprobs=logprobs_result.input_token_logprobs,

python/sglang/srt/managers/schedule_batch.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import enum
 
+from sglang.srt.dllm.config import DllmConfig
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 
 # Copyright 2023-2024 SGLang Team
@@ -442,6 +443,7 @@ class Req:
         sampling_params: SamplingParams,
         return_logprob: bool = False,
         top_logprobs_num: int = 0,
+        dllm_config: Optional[DllmConfig] = None,
         token_ids_logprob: List[int] = None,
         stream: bool = False,
         origin_input_ids_unpadded: Optional[Tuple[int]] = None,
@@ -683,6 +685,11 @@ class Req:
         # For Matryoshka embeddings
         self.dimensions = dimensions
 
+        # For diffusion LLM
+        self.dllm_ids = []
+        self.dllm_block_offset = 0
+        self.dllm_config = dllm_config
+
     @property
     def seqlen(self):
         return len(self.origin_input_ids) + len(self.output_ids)
@@ -751,8 +758,28 @@ class Req:
         # Whether request reached finished condition
         return self.finished_reason is not None
 
+    def is_dllm(self):
+        return self.dllm_config is not None
+
     def init_next_round_input(self, tree_cache: Optional[BasePrefixCache] = None):
-        self.fill_ids = self.origin_input_ids + self.output_ids
+        if self.is_dllm():
+            if not self.fill_ids:
+                self.dllm_ids = (
+                    self.origin_input_ids
+                    + [
+                        self.dllm_config.mask_id,
+                    ]
+                    * self.dllm_config.block_size
+                )
+            else:
+                self.dllm_block_offset += self.dllm_config.block_size
+                self.dllm_ids += [
+                    self.dllm_config.mask_id
+                ] * self.dllm_config.block_size
+            self.fill_ids = self.dllm_ids
+        else:
+            self.fill_ids = self.origin_input_ids + self.output_ids
+
         input_len = len(self.fill_ids)
         # NOTE: the matched length is at most 1 less than the input length to enable logprob computation
         max_prefix_len = input_len - 1
@@ -1127,6 +1154,9 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
     # hicache pointer for synchronizing data loading from CPU to GPU
     hicache_consumer_index: int = -1
 
+    # Diffusion LLM
+    dllm_config: Optional[DllmConfig] = None
+
     @classmethod
     def init_new(
         cls,
@@ -1138,6 +1168,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
         enable_overlap: bool,
         spec_algorithm: SpeculativeAlgorithm,
         chunked_req: Optional[Req] = None,
+        dllm_config: Optional[DllmConfig] = None,
     ):
         return_logprob = any(req.return_logprob for req in reqs)
 
@@ -1166,6 +1197,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             return_hidden_states=any(req.return_hidden_states for req in reqs),
             is_prefill_only=all(req.is_prefill_only for req in reqs),
             chunked_req=chunked_req,
+            dllm_config=dllm_config,
         )
 
     def batch_size(self):
@@ -1174,6 +1206,9 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
     def is_empty(self):
         return len(self.reqs) == 0
 
+    def is_dllm(self):
+        return self.dllm_config is not None
+
     def prepare_encoder_info_extend(self, input_ids: List[int], seq_lens: List[int]):
         self.encoder_lens_cpu = []
         self.encoder_cached = []
@@ -1886,6 +1921,8 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             extend_input_logprob_token_ids=self.extend_input_logprob_token_ids,
             is_prefill_only=self.is_prefill_only,
             dimensions=self.dimensions,
+            dllm_block_offsets=[req.dllm_block_offset for req in self.reqs],
+            dllm_config=self.dllm_config,
         )
 
     def copy(self):
@@ -1999,3 +2036,7 @@ class ModelWorkerBatch:
 
     # Whether this batch is prefill-only (no token generation needed)
     is_prefill_only: bool = False
+
+    # Diffusion LLM
+    dllm_block_offsets: Optional[List[int]] = None
+    dllm_config: Optional[DllmConfig] = None

python/sglang/srt/managers/scheduler.py

@@ -60,6 +60,7 @@ from sglang.srt.disaggregation.utils import (
     prepare_abort,
 )
 from sglang.srt.distributed import get_pp_group, get_world_group
+from sglang.srt.dllm.config import DllmConfig
 from sglang.srt.environ import envs
 from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
 from sglang.srt.layers.dp_attention import compute_dp_attention_world_info
@@ -287,6 +288,9 @@ class Scheduler(
         # Init model config
         self.model_config = ModelConfig.from_server_args(server_args)
 
+        # Init diffusion LLM config
+        self.dllm_config = DllmConfig.from_server_args(server_args)
+
         # Init inter-process communication
         self.init_sockets(server_args, port_args)
 
@@ -449,6 +453,10 @@ class Scheduler(
 
         # Init chunked prefill
         self.chunked_prefill_size = server_args.chunked_prefill_size
+        if self.dllm_config is not None:
+            # We currently leverage chunked prefill to implement block diffusion
+            # for diffusion LLM.
+            self.chunked_prefill_size = self.dllm_config.block_size
         if self.chunked_prefill_size <= 0:  # -1 means disable
             self.chunked_prefill_size = None
         self.chunked_req = None
@@ -1284,6 +1292,7 @@ class Scheduler(
                     self.metrics_collector if self.enable_metrics else None
                 ),
                 http_worker_ipc=recv_req.http_worker_ipc,
+                dllm_config=self.dllm_config,
             )
             req.tokenizer = self.tokenizer
 
@@ -1600,6 +1609,10 @@ class Scheduler(
             self.handle_embedding_request(tokenized_req)
 
     def get_next_batch_to_run(self) -> Optional[ScheduleBatch]:
+        if self.dllm_config is not None:
+            if self.chunked_req is not None and self.chunked_req.finished():
+                self.chunked_req = None
+
         # Merge the prefill batch into the running batch
         chunked_req_to_exclude = set()
         if self.chunked_req:
@@ -1832,6 +1845,7 @@ class Scheduler(
             self.enable_overlap,
             self.spec_algorithm,
             chunked_req=self.chunked_req,
+            dllm_config=self.dllm_config,
         )
         if self.enable_hierarchical_cache:
             # todo (zhiqiang): disable cuda graph execution if hicache loading triggered
@@ -2064,7 +2078,10 @@ class Scheduler(
             self.process_batch_result_decode(batch, result)
             trace_slice_batch(RequestStage.DECODE_LOOP, batch.reqs)
         elif batch.forward_mode.is_extend():
-            self.process_batch_result_prefill(batch, result)
+            if batch.is_dllm():
+                self.process_batch_result_dllm(batch, result)
+            else:
+                self.process_batch_result_prefill(batch, result)
         elif batch.forward_mode.is_prebuilt():
             self.process_batch_result_prebuilt(batch)
         elif batch.forward_mode.is_idle():

python/sglang/srt/managers/scheduler_output_processor_mixin.py

@@ -281,6 +281,36 @@ class SchedulerOutputProcessorMixin:
 
         return predict_tokens
 
+    def process_batch_result_dllm(
+        self: Scheduler,
+        batch: ScheduleBatch,
+        result: GenerationBatchResult,
+    ):
+        if result.copy_done is not None:
+            result.copy_done.synchronize()
+
+        next_token_ids = result.next_token_ids.tolist()
+        self.num_generated_tokens += len(next_token_ids)
+
+        self.token_to_kv_pool_allocator.free_group_begin()
+
+        assert len(batch.reqs) == 1, "batch size is currently expected to be 1"
+        req = batch.reqs[0]
+
+        for next_token_id in next_token_ids:
+            req.output_ids.append(next_token_id)
+            req.check_finished()
+
+            if req.finished():
+                release_kv_cache(req, self.tree_cache)
+                req.time_stats.completion_time = time.perf_counter()
+                break
+
+            self.tree_cache.cache_unfinished_req(req)
+
+        self.stream_output(batch.reqs, batch.return_logprob)
+        self.token_to_kv_pool_allocator.free_group_end()
+
     def process_batch_result_decode(
         self: Scheduler,
         batch: ScheduleBatch,

python/sglang/srt/managers/tp_worker.py

@@ -22,6 +22,7 @@ import torch
 
 from sglang.srt.configs.model_config import ModelConfig
 from sglang.srt.distributed import get_pp_group, get_world_group
+from sglang.srt.dllm.algorithm.base import DllmAlgorithm
 from sglang.srt.managers.io_struct import (
     DestroyWeightsUpdateGroupReqInput,
     GetWeightsByNameReqInput,
@@ -234,6 +235,9 @@ class TpModelWorker(BaseTpWorker):
             is_draft_model=is_draft_worker,
         )
 
+        if server_args.dllm_algorithm is not None:
+            self.dllm_algorithm = DllmAlgorithm.from_server_args(server_args)
+
         self._model_runner = ModelRunner(
             model_config=self.model_config,
             mem_fraction_static=server_args.mem_fraction_static,
@@ -340,6 +344,9 @@ class TpModelWorker(BaseTpWorker):
             self.model_runner.token_to_kv_pool.size,
         )
 
+    def is_dllm(self):
+        return hasattr(self, "dllm_algorithm")
+
     def forward_batch_generation(
         self,
         model_worker_batch: ModelWorkerBatch,
@@ -368,6 +375,16 @@ class TpModelWorker(BaseTpWorker):
             )
 
         if self.pp_group.is_last_rank:
+            if self.is_dllm():
+                logits_output, next_token_ids, can_run_cuda_graph = (
+                    self.dllm_algorithm.run(self.model_runner, forward_batch)
+                )
+                return GenerationBatchResult(
+                    logits_output=logits_output,
+                    next_token_ids=next_token_ids,
+                    can_run_cuda_graph=can_run_cuda_graph,
+                )
+
             logits_output, can_run_cuda_graph = self.model_runner.forward(
                 forward_batch,
                 pp_proxy_tensors=pp_proxy_tensors,

python/sglang/srt/model_executor/forward_batch_info.py

@@ -441,8 +441,17 @@ class ForwardBatch:
             )
             return ret
 
-        # Override the positions with spec_info
-        if (
+        # Override the positions with diffusion LLM or spec_info
+        if batch.dllm_config is not None:
+            block_size = batch.dllm_config.block_size
+            ret.positions = torch.tensor(
+                [
+                    [i for i in range(block_offset, block_offset + block_size)]
+                    for block_offset in batch.dllm_block_offsets
+                ],
+                dtype=torch.int32,
+            ).to(device, non_blocking=True)
+        elif (
             ret.spec_info is not None
             and getattr(ret.spec_info, "positions", None) is not None
         ):

python/sglang/srt/models/llada2.py

@@ -0,0 +1,941 @@
+# coding=utf-8
+# Copyright 2023 Antgroup 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.
+"""SGLang LLaDA2MoeModelLM model."""
+import logging
+from typing import Iterable, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import PretrainedConfig
+
+from sglang.srt.distributed import (
+    get_pp_group,
+    get_tensor_model_parallel_world_size,
+    parallel_state,
+    tensor_model_parallel_all_reduce,
+)
+from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
+from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation
+from sglang.srt.eplb.expert_location_dispatch import ExpertLocationDispatchInfo
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.communicator import (
+    LayerCommunicator,
+    LayerScatterModes,
+    enable_moe_dense_fully_dp,
+)
+from sglang.srt.layers.dp_attention import (
+    get_attention_dp_size,
+    get_attention_tp_rank,
+    get_attention_tp_size,
+    is_dp_attention_enabled,
+)
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.moe import get_deepep_mode, get_moe_a2a_backend
+from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class
+from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
+from sglang.srt.layers.moe.token_dispatcher import DeepEPDispatcher
+from sglang.srt.layers.moe.topk import TopK
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.radix_attention import AttentionType, RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
+from sglang.srt.layers.utils import PPMissingLayer
+from sglang.srt.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.utils import (
+    create_fused_set_kv_buffer_arg,
+    enable_fused_set_kv_buffer,
+)
+from sglang.srt.server_args import get_global_server_args
+from sglang.srt.utils import add_prefix, is_cuda, is_non_idle_and_non_empty, make_layers
+
+LoraConfig = None
+logger = logging.getLogger(__name__)
+_is_cuda = is_cuda()
+
+
+class LLaDA2MoeMLP(nn.Module):
+    def __init__(
+        self,
+        intermediate_size: int,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        reduce_results: Optional[bool] = True,
+        prefix: str = "",
+        tp_rank: Optional[int] = None,
+        tp_size: Optional[int] = None,
+    ) -> None:
+        super().__init__()
+        self.tp_size = tp_size
+
+        self.gate_up_proj = MergedColumnParallelLinear(
+            config.hidden_size,
+            [intermediate_size] * 2,
+            bias=config.use_bias,
+            quant_config=quant_config,
+            prefix=add_prefix("gate_up_proj", prefix),
+            tp_rank=tp_rank,
+            tp_size=tp_size,
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            config.hidden_size,
+            bias=config.use_bias,
+            reduce_results=reduce_results,
+            quant_config=quant_config,
+            prefix=add_prefix("down_proj", prefix),
+            tp_rank=tp_rank,
+            tp_size=tp_size,
+        )
+
+        if config.hidden_act != "silu":
+            raise ValueError("Unsupported activation. Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        forward_batch: Optional[ForwardBatch] = None,
+        use_reduce_scatter: bool = False,
+    ) -> torch.Tensor:
+        if (self.tp_size == 1) and hidden_states.shape[0] == 0:
+            return hidden_states
+
+        gate_up, _ = self.gate_up_proj(hidden_states)
+        hidden_states = self.act_fn(gate_up)
+        hidden_states, _ = self.down_proj(
+            hidden_states, skip_all_reduce=use_reduce_scatter
+        )
+        return hidden_states
+
+
+class LLaDA2MoeGate(nn.Module):
+    def __init__(
+        self,
+        config,
+        params_dtype: Optional[torch.dtype] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        if params_dtype is None:
+            params_dtype = torch.get_default_dtype()
+        self.params_dtype = params_dtype
+        self.weight = nn.Parameter(
+            torch.empty(
+                (config.num_experts, config.hidden_size),
+                dtype=self.params_dtype,
+            ),
+        )
+        if getattr(config, "moe_router_enable_expert_bias", False):
+            self.expert_bias = nn.Parameter(
+                torch.empty((config.num_experts,), dtype=torch.float32),
+            )
+        else:
+            self.expert_bias = None
+
+    def forward(self, hidden_states):
+        logits = F.linear(hidden_states.to(self.weight.dtype), self.weight, None).to(
+            hidden_states.dtype
+        )
+        return logits
+
+
+class LLaDA2MoeSparseMoeBlock(nn.Module):
+    def __init__(
+        self,
+        layer_id: int,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        alt_stream: Optional[torch.cuda.Stream] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.layer_id = layer_id
+        self.alt_stream = alt_stream
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.top_k = config.num_experts_per_tok
+        self.norm_topk_prob = config.norm_topk_prob
+        self.hidden_size = config.hidden_size
+        self.num_shared_experts = config.num_shared_experts
+        self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0)
+        self.score_function = getattr(config, "score_function", None)
+
+        if config.hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {config.hidden_act}. "
+                "Only silu is supported for now."
+            )
+
+        # Gate always runs at half / full precision for now.
+        router_dtype = getattr(config, "router_dtype", None)
+        if router_dtype is None:
+            self.router_dtype = None
+        elif router_dtype == "fp32":
+            self.router_dtype = torch.float32
+        else:
+            self.router_dtype = torch.bfloat16
+
+        # TODO global_server_args.ep_num_redundant_experts is used for eplb, not supported now
+        assert get_global_server_args().ep_num_redundant_experts == 0
+        # check group topk
+        self.num_expert_group = getattr(config, "n_group", 0)
+        self.topk_group = getattr(config, "topk_group", 0)
+        if self.num_expert_group > 0 or self.topk_group > 0:
+            assert (
+                self.num_expert_group > 0
+                and 0 < self.topk_group <= self.num_expert_group
+            )
+            self.use_grouped_topk = True
+        else:
+            self.num_expert_group = self.topk_group = None
+            self.use_grouped_topk = False
+
+        self.num_experts = (
+            config.num_experts + get_global_server_args().ep_num_redundant_experts
+        )
+
+        self.gate = LLaDA2MoeGate(
+            config=config,
+            params_dtype=self.router_dtype,
+            prefix=add_prefix("gate", prefix),
+        )
+        self.correction_bias = (
+            self.gate.expert_bias.data if self.gate.expert_bias is not None else None
+        )
+
+        if self.score_function is not None:
+            assert (
+                self.score_function == "softmax" and self.correction_bias is None
+            ) or (
+                self.score_function == "sigmoid" and self.correction_bias is not None
+            ), "score_function and correction_bias should be in 2 combination (softmax, None) or (sigmoid, not None)"
+
+        self.topk = TopK(
+            top_k=self.top_k,
+            renormalize=self.norm_topk_prob,
+            use_grouped_topk=self.use_grouped_topk,
+            num_expert_group=self.num_expert_group,
+            # num_fused_shared_experts=self.num_fused_shared_experts,
+            topk_group=self.topk_group,
+            correction_bias=self.correction_bias,
+            routed_scaling_factor=self.routed_scaling_factor,
+        )
+
+        self.experts = get_moe_impl_class(quant_config)(
+            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,
+            routed_scaling_factor=self.routed_scaling_factor,
+            prefix=add_prefix("experts", prefix),
+        )
+        # shared expert
+        if config.num_shared_experts is not None:
+            if hasattr(config, "moe_shared_expert_intermediate_size"):
+                intermediate_size = config.moe_shared_expert_intermediate_size
+            else:
+                intermediate_size = config.moe_intermediate_size
+            intermediate_size *= config.num_shared_experts
+            # disable tp for shared experts when enable deepep moe
+            self.shared_experts = LLaDA2MoeMLP(
+                intermediate_size=intermediate_size,
+                config=config,
+                quant_config=quant_config,
+                reduce_results=False,
+                prefix=add_prefix("shared_experts", prefix),
+                **(
+                    dict(tp_rank=0, tp_size=1)
+                    if get_moe_a2a_backend().is_deepep()
+                    else {}
+                ),
+            )
+        # dispatcher
+        if get_moe_a2a_backend().is_deepep():
+            # TODO: we will support tp < ep in the future
+            self.ep_size = get_tensor_model_parallel_world_size()
+
+            self.deepep_dispatcher = DeepEPDispatcher(
+                group=parallel_state.get_tp_group().device_group,
+                router_topk=self.top_k,
+                permute_fusion=True,
+                num_experts=self.num_experts,
+                num_local_experts=config.num_experts // self.tp_size,
+                hidden_size=config.hidden_size,
+                params_dtype=config.torch_dtype,
+                deepep_mode=get_deepep_mode(),
+                async_finish=True,  # TODO
+                return_recv_hook=True,
+            )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        forward_batch: Optional[ForwardBatch] = None,
+        use_reduce_scatter: bool = False,
+    ) -> torch.Tensor:
+        if not get_moe_a2a_backend().is_deepep():
+            return self.forward_normal(hidden_states, use_reduce_scatter)
+        else:
+            return self.forward_deepep(hidden_states, forward_batch)
+
+    def get_moe_weights(self):
+        return [
+            x.data
+            for name, x in self.experts.named_parameters()
+            if name not in ["correction_bias"]
+        ]
+
+    def _forward_shared_experts(self, hidden_states: torch.Tensor):
+        shared_output = None
+        if self.num_shared_experts > 0:
+            shared_output = self.shared_experts(hidden_states)
+        return shared_output
+
+    def _forward_router_experts(self, hidden_states: torch.Tensor):
+        # router_logits: (num_tokens, n_experts)
+        router_logits = self.gate(hidden_states)
+        topk_output = self.topk(hidden_states, router_logits)
+        return self.experts(hidden_states, topk_output)
+
+    def forward_normal_dual_stream(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        current_stream = torch.cuda.current_stream()
+        self.alt_stream.wait_stream(current_stream)
+        shared_output = self._forward_shared_experts(hidden_states.clone())
+
+        with torch.cuda.stream(self.alt_stream):
+            router_output = self._forward_router_experts(hidden_states)
+        current_stream.wait_stream(self.alt_stream)
+
+        return router_output, shared_output
+
+    def forward_normal(
+        self,
+        hidden_states: torch.Tensor,
+        use_reduce_scatter: bool = False,
+    ) -> torch.Tensor:
+        num_tokens, hidden_size = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_size)
+
+        DUAL_STREAM_TOKEN_THRESHOLD = 1024
+        if (
+            self.alt_stream is not None
+            and hidden_states.shape[0] > 0
+            and hidden_states.shape[0] <= DUAL_STREAM_TOKEN_THRESHOLD
+            and get_is_capture_mode()
+        ):
+            final_hidden_states, shared_output = self.forward_normal_dual_stream(
+                hidden_states
+            )
+        else:
+            shared_output = self._forward_shared_experts(hidden_states)
+            final_hidden_states = self._forward_router_experts(hidden_states)
+
+        if self.num_shared_experts > 0:
+            final_hidden_states = final_hidden_states + shared_output
+
+        if self.tp_size > 1 and not use_reduce_scatter:
+            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
+        return final_hidden_states.view(num_tokens, hidden_size)
+
+    def forward_deepep(
+        self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
+    ) -> torch.Tensor:
+        shared_output = None
+        forward_mode = forward_batch.forward_mode
+        if is_non_idle_and_non_empty(forward_mode, hidden_states):
+            router_logits = self.gate(hidden_states)
+            if self.num_shared_experts > 0:
+                shared_output = self.shared_experts(hidden_states)
+
+            topk_output = self.topk(
+                hidden_states,
+                router_logits,
+                num_token_non_padded=forward_batch.num_token_non_padded,
+                expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
+                    layer_id=self.layer_id,
+                ),
+            )
+        else:
+            topk_output = self.topk.empty_topk_output(hidden_states.device)
+
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states,
+            topk_output=topk_output,
+        )
+
+        if shared_output is not None:
+            final_hidden_states += shared_output
+        return final_hidden_states
+
+
+class LLaDA2MoeAttention(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+        reduce_results: bool = True,
+        prefix: str = "",
+        alt_stream: Optional[torch.cuda.Stream] = None,
+    ):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.total_num_heads = config.num_attention_heads
+        self.total_kv_heads = config.num_key_value_heads
+        self.dp_size = get_attention_dp_size()
+        attn_tp_rank = get_attention_tp_rank()
+        attn_tp_size = get_attention_tp_size()
+
+        assert self.total_num_heads % attn_tp_size == 0
+        if self.total_kv_heads >= attn_tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_kv_heads % attn_tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert attn_tp_size % self.total_kv_heads == 0
+        assert self.total_num_heads >= self.total_kv_heads
+
+        self.num_heads = self.total_num_heads // attn_tp_size
+        self.head_dim = config.head_dim or (self.hidden_size // self.total_num_heads)
+        self.q_size = self.head_dim * self.num_heads
+
+        self.num_kv_heads = max(1, self.total_kv_heads // attn_tp_size)
+        self.kv_size = max(1, self.num_kv_heads * self.head_dim)
+
+        self.scale = self.head_dim**-0.5
+
+        self.use_qk_norm = getattr(config, "use_qk_norm", True)
+
+        self.query_key_value = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_kv_heads,
+            bias=(config.use_bias or config.use_qkv_bias),
+            quant_config=quant_config,
+            prefix=add_prefix("query_key_value", prefix),
+            tp_rank=attn_tp_rank,
+            tp_size=attn_tp_size,
+        )
+
+        if self.use_qk_norm:
+            self.query_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+            self.key_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+
+        self.dense = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            self.hidden_size,
+            bias=config.use_bias,
+            quant_config=quant_config,
+            reduce_results=reduce_results,
+            prefix=add_prefix("dense", prefix),
+            tp_rank=attn_tp_rank,
+            tp_size=attn_tp_size,
+        )
+
+        if hasattr(config, "partial_rotary_factor"):
+            self.rotary_dim = int(self.head_dim * config.partial_rotary_factor)
+        elif hasattr(config, "rotary_dim"):
+            self.rotary_dim = config.rotary_dim
+        else:
+            self.rotary_dim = self.head_dim
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.rotary_dim,
+            max_position=config.max_position_embeddings,
+            base=config.rope_theta,
+            rope_scaling=config.rope_scaling,
+        )
+
+        self.attn = RadixAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scale,
+            num_kv_heads=self.num_kv_heads,
+            layer_id=layer_id,
+            attn_type=AttentionType.ENCODER_ONLY,
+            prefix=add_prefix("attn", prefix),
+        )
+
+        self.alt_stream = alt_stream
+
+    def _apply_qk_norm(
+        self, q: torch.Tensor, k: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # overlap qk norm
+        if self.alt_stream is not None and get_is_capture_mode():
+            current_stream = torch.cuda.current_stream()
+            self.alt_stream.wait_stream(current_stream)
+            q_by_head = q.reshape(-1, self.head_dim)
+            q_by_head = self.query_layernorm(q_by_head)
+            with torch.cuda.stream(self.alt_stream):
+                k_by_head = k.reshape(-1, self.head_dim)
+                k_by_head = self.key_layernorm(k_by_head)
+            current_stream.wait_stream(self.alt_stream)
+        else:
+            q_by_head = q.reshape(-1, self.head_dim)
+            q_by_head = self.query_layernorm(q_by_head)
+            k_by_head = k.reshape(-1, self.head_dim)
+            k_by_head = self.key_layernorm(k_by_head)
+        q = q_by_head.view(q.shape)
+        k = k_by_head.view(k.shape)
+        return q, k
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        if hidden_states.shape[0] == 0:
+            return hidden_states
+        qkv, _ = self.query_key_value(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        if self.use_qk_norm:
+            q, k = self._apply_qk_norm(q, k)
+        q, k = self.rotary_emb(
+            positions,
+            q,
+            k,
+            fused_set_kv_buffer_arg=(
+                create_fused_set_kv_buffer_arg(
+                    value=v,
+                    layer=self.attn,
+                    forward_batch=forward_batch,
+                )
+                if enable_fused_set_kv_buffer(forward_batch)
+                else None
+            ),
+        )
+        context_layer = self.attn(
+            q,
+            k,
+            v,
+            forward_batch,
+            save_kv_cache=not enable_fused_set_kv_buffer(forward_batch),
+        )
+        attn_output, _ = self.dense(context_layer)
+        return attn_output
+
+
+class LLaDA2MoeBlock(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        alt_stream: Optional[torch.cuda.Stream] = None,
+    ):
+        super().__init__()
+        hidden_size = config.hidden_size
+
+        self.input_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps)
+        self.dp_size = get_attention_dp_size()
+        self.attention = LLaDA2MoeAttention(
+            config,
+            layer_id,
+            quant_config,
+            reduce_results=False,
+            prefix=add_prefix("attention", prefix),
+            alt_stream=alt_stream,
+        )
+        self.layer_id = layer_id
+        self.attn_tp_size = get_attention_tp_size()
+        self.attn_tp_rank = get_attention_tp_rank()
+
+        self.is_layer_sparse = self._is_layer_sparse(config, layer_id=layer_id)
+        is_previous_layer_sparse = self._is_layer_sparse(config, layer_id=layer_id - 1)
+
+        self.layer_scatter_modes = LayerScatterModes.init_new(
+            layer_id=layer_id,
+            num_layers=config.num_hidden_layers,
+            is_layer_sparse=self.is_layer_sparse,
+            is_previous_layer_sparse=is_previous_layer_sparse,
+        )
+
+        self.is_last_layer = self.layer_id == config.num_hidden_layers - 1
+
+        if self.is_layer_sparse:
+            self.mlp = LLaDA2MoeSparseMoeBlock(
+                layer_id=layer_id,
+                config=config,
+                quant_config=quant_config,
+                alt_stream=alt_stream,
+                prefix=add_prefix("mlp", prefix),
+            )
+        else:
+            if enable_moe_dense_fully_dp():
+                mlp_tp_rank, mlp_tp_size = 0, 1
+            else:
+                mlp_tp_rank, mlp_tp_size = None, None
+            self.mlp = LLaDA2MoeMLP(
+                intermediate_size=config.intermediate_size,
+                config=config,
+                quant_config=quant_config,
+                prefix=add_prefix("mlp", prefix),
+                tp_rank=mlp_tp_rank,
+                tp_size=mlp_tp_size,
+            )
+
+        self.post_attention_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps)
+
+        self.layer_communicator = LayerCommunicator(
+            layer_scatter_modes=self.layer_scatter_modes,
+            input_layernorm=self.input_layernorm,
+            post_attention_layernorm=self.post_attention_layernorm,
+            allow_reduce_scatter=True,
+        )
+
+    def _is_layer_sparse(self, config: PretrainedConfig, layer_id: int) -> bool:
+        return (
+            config.num_experts is not None and layer_id >= config.first_k_dense_replace
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+        residual: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        hidden_states, residual = self.layer_communicator.prepare_attn(
+            hidden_states=hidden_states,
+            residual=residual,
+            forward_batch=forward_batch,
+        )
+
+        hidden_states = self.attention(
+            positions=positions,
+            hidden_states=hidden_states,
+            forward_batch=forward_batch,
+        )
+
+        hidden_states, residual = self.layer_communicator.prepare_mlp(
+            hidden_states=hidden_states,
+            residual=residual,
+            forward_batch=forward_batch,
+        )
+
+        # For DP with padding, reduce scatter can be used instead of all-reduce.
+        use_reduce_scatter = self.layer_communicator.should_use_reduce_scatter(
+            forward_batch
+        )
+
+        hidden_states = self.mlp(hidden_states, forward_batch, use_reduce_scatter)
+
+        hidden_states, residual = self.layer_communicator.postprocess_layer(
+            hidden_states=hidden_states,
+            residual=residual,
+            forward_batch=forward_batch,
+        )
+
+        return hidden_states, residual
+
+
+class LLaDA2MoeModel(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        alt_stream: Optional[torch.cuda.Stream] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.pp_group = get_pp_group()
+        self.config = config
+        self.vocab_size = config.vocab_size
+        self.embed_dim = config.hidden_size
+        if self.pp_group.is_first_rank:
+            self.word_embeddings = VocabParallelEmbedding(
+                self.vocab_size,
+                self.embed_dim,
+                quant_config=quant_config,
+                prefix=add_prefix("word_embeddings", prefix),
+                enable_tp=not is_dp_attention_enabled(),
+            )
+        else:
+            self.word_embeddings = PPMissingLayer()
+
+        self.embedding_dropout = torch.nn.Dropout(config.embedding_dropout)
+
+        self.layers, self.start_layer, self.end_layer = make_layers(
+            config.num_hidden_layers,
+            lambda idx, prefix: LLaDA2MoeBlock(
+                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(self.embed_dim, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer(return_tuple=True)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+        pp_proxy_tensors: Optional[PPProxyTensors] = None,
+    ) -> Union[torch.Tensor, PPProxyTensors]:
+        if self.pp_group.is_first_rank:
+            if input_embeds is None:
+                hidden_states = self.word_embeddings(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"]
+
+        for i in range(self.start_layer, self.end_layer):
+            with get_global_expert_distribution_recorder().with_current_layer(i):
+                layer = self.layers[i]
+                hidden_states, residual = layer(
+                    positions,
+                    hidden_states,
+                    forward_batch,
+                    residual,
+                )
+        if not self.pp_group.is_last_rank:
+            return PPProxyTensors(
+                {
+                    "hidden_states": hidden_states,
+                    "residual": residual,
+                }
+            )
+        else:
+            if not forward_batch.forward_mode.is_idle():
+                if residual is None:
+                    hidden_states = self.norm(hidden_states)
+                else:
+                    hidden_states, _ = self.norm(hidden_states, residual)
+            return hidden_states
+
+
+class LLaDA2MoeModelLM(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.pp_group = get_pp_group()
+        self.config = config
+        self.quant_config = quant_config
+        alt_stream = torch.cuda.Stream() if _is_cuda else None
+
+        self.model = LLaDA2MoeModel(
+            config,
+            quant_config,
+            alt_stream=alt_stream,
+            prefix=add_prefix("model", ""),
+        )
+
+        if config.tie_word_embeddings:
+            self.lm_head = self.model.word_embeddings
+        else:
+            # TODO something wrong with ParallelLMHead with DP attention enabled
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=add_prefix("lm_head", prefix),
+                use_attn_tp_group=get_global_server_args().enable_dp_lm_head,
+            )
+        self.logits_processor = LogitsProcessor(config, return_full_logits=True)
+
+    @property
+    def start_layer(self):
+        return self.model.start_layer
+
+    @property
+    def end_layer(self):
+        return self.model.end_layer
+
+    def get_embed_and_head(self):
+        """Used by the eagle_worker."""
+        return self.model.word_embeddings.weight, self.lm_head.weight
+
+    def set_embed_and_head(self, embed, head):
+        """Used by the eagle_worker."""
+        del self.model.word_embeddings.weight
+        del self.lm_head.weight
+        self.model.word_embeddings.weight = embed
+        self.lm_head.weight = head
+        torch.cuda.empty_cache()
+        torch.cuda.synchronize()
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+        pp_proxy_tensors: Optional[PPProxyTensors] = None,
+    ) -> torch.Tensor:
+        hidden_states = self.model(
+            input_ids,
+            positions,
+            forward_batch,
+            input_embeds,
+            pp_proxy_tensors=pp_proxy_tensors,
+        )
+        if self.pp_group.is_last_rank:
+            return self.logits_processor(
+                input_ids, hidden_states, self.lm_head, forward_batch
+            )
+        else:
+            return hidden_states
+
+    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 = FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.num_experts,
+        )
+
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+            if (
+                ("v_head" in name)
+                or ("inv_freq" in name)
+                or (self.config.tie_word_embeddings and "lm_head" in name)
+            ):
+                continue
+
+            if (
+                hasattr(self.config, "norm_head")
+                and self.config.norm_head
+                and "lm_head.weight" in name
+            ):
+                import torch.nn.functional as F
+
+                loaded_weight = F.normalize(loaded_weight, dim=0, p=2, eps=1e-7)
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                # We have mlp.experts[0].gate_proj in the checkpoint.
+                # Since we handle the experts below in expert_params_mapping,
+                # we need to skip here BEFORE we update the name, otherwise
+                # name will be updated to mlp.experts[0].gate_up_proj, which
+                # will then be updated below in expert_params_mapping
+                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+                if "mlp.experts" in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    if name not in params_dict:
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(
+                        param,
+                        loaded_weight,
+                        name,
+                        shard_id=shard_id,
+                        expert_id=expert_id,
+                    )
+                    break
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    if name not in params_dict:
+                        continue
+
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+
+        self.routed_experts_weights_of_layer = {
+            layer_id: layer.mlp.get_moe_weights()
+            for layer_id, layer in enumerate(self.model.layers)
+            if not isinstance(layer, PPMissingLayer)
+            and isinstance(layer.mlp, LLaDA2MoeSparseMoeBlock)
+        }
+
+    @classmethod
+    def get_model_config_for_expert_location(cls, config):
+        num_groups = getattr(config, "n_group", 0)
+        return ModelConfigForExpertLocation(
+            num_layers=config.num_hidden_layers,
+            num_logical_experts=config.num_experts,
+            num_groups=None if num_groups == 0 else num_groups,
+        )
+
+
+EntryClass = LLaDA2MoeModelLM

python/sglang/srt/server_args.py

@@ -240,6 +240,10 @@ class ServerArgs:
     revision: Optional[str] = None
     model_impl: str = "auto"
 
+    # Diffusion LLM
+    dllm_algorithm: Optional[str] = None
+    dllm_block_size: Optional[int] = None
+
     # HTTP server
     host: str = "127.0.0.1"
     port: int = 30000
@@ -663,6 +667,9 @@ class ServerArgs:
         # Handle exporting request-level metrics.
         self._handle_request_metrics_exporters()
 
+        # Handle diffusion LLM inference.
+        self._handle_dllm_inference()
+
         # Handle any other necessary validations.
         self._handle_other_validations()
 
@@ -1974,6 +1981,30 @@ class ServerArgs:
                 "--export-metrics-to-file-dir is required when --export-metrics-to-file is enabled"
             )
 
+    def _handle_dllm_inference(self):
+        if self.dllm_algorithm is None:
+            return
+        if not self.disable_cuda_graph:
+            logger.warning(
+                "Cuda graph is disabled because of using diffusion LLM inference"
+            )
+            self.disable_cuda_graph = True
+        if not self.disable_overlap_schedule:
+            logger.warning(
+                "Overlap schedule is disabled because of using diffusion LLM inference"
+            )
+            self.disable_overlap_schedule = True
+        if not self.disable_radix_cache:
+            logger.warning(
+                "Radix cache is disabled because of using diffusion LLM inference"
+            )
+            self.disable_radix_cache = True
+        if not self.pp_size > 1:
+            logger.warning(
+                "Pipeline parallelism is disabled because of using diffusion LLM inference"
+            )
+            self.pp_size = 1
+
     def _handle_other_validations(self):
         # Handle model inference tensor dump.
         if self.debug_tensor_dump_output_folder is not None:
@@ -2093,6 +2124,20 @@ class ServerArgs:
             "implementation.\n",
         )
 
+        # Diffusion LLM
+        parser.add_argument(
+            "--dllm-algorithm",
+            type=str,
+            default=ServerArgs.dllm_algorithm,
+            help="The diffusion LLM algorithm.",
+        )
+        parser.add_argument(
+            "--dllm-block-size",
+            type=int,
+            default=ServerArgs.dllm_block_size,
+            help="The number of tokens processed in each iteration of the block diffusion LLM.",
+        )
+
         # HTTP server
         parser.add_argument(
             "--host",