Upload 4 files

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLMEagle3"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 1,
+  "num_key_value_heads":4 ,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936,
+  "draft_vocab_size": 32000
+}
+

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:14e0dd4603c30e14a3664bfdc19aecbfd02e7a86a8c9c301687120f8e1bb2e9c
+size 339239102

qwen3_moe.py

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+# Adapted from qwen2_moe.py
+
+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+
+"""Inference-only Qwen3MoE model compatible with HuggingFace weights."""
+
+import logging
+from typing import Any, Dict, Iterable, Optional, Tuple, Union
+
+import torch
+from torch import nn
+
+from sglang.srt.distributed import (
+    get_pp_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+    parallel_state,
+    split_tensor_along_last_dim,
+    tensor_model_parallel_all_gather,
+    tensor_model_parallel_all_reduce,
+)
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.communicator import LayerCommunicator, LayerScatterModes
+from sglang.srt.layers.dp_attention import (
+    attn_tp_all_gather,
+    attn_tp_reduce_scatter,
+    dp_gather_partial,
+    dp_scatter,
+    get_attention_tp_rank,
+    get_attention_tp_size,
+    get_local_attention_dp_size,
+)
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
+from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class
+from sglang.srt.layers.moe.ep_moe.token_dispatcher import DeepEPDispatcher
+from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
+from sglang.srt.layers.moe.topk import select_experts
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
+from sglang.srt.layers.utils import get_layer_id
+from sglang.srt.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.managers.expert_distribution import (
+    get_global_expert_distribution_recorder,
+)
+from sglang.srt.managers.expert_location import ModelConfigForExpertLocation
+from sglang.srt.managers.expert_location_dispatch import ExpertLocationDispatchInfo
+from sglang.srt.managers.schedule_batch import global_server_args_dict
+from sglang.srt.model_executor.forward_batch_info import (
+    ForwardBatch,
+    ForwardMode,
+    PPProxyTensors,
+)
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.qwen2_moe import Qwen2MoeMLP as Qwen3MoeMLP
+from sglang.srt.models.qwen2_moe import Qwen2MoeModel
+from sglang.srt.two_batch_overlap import MaybeTboDeepEPDispatcher, model_forward_maybe_tbo, ScatterMode
+from sglang.srt.utils import DeepEPMode, add_prefix, is_non_idle_and_non_empty
+
+Qwen3MoeConfig = None
+
+logger = logging.getLogger(__name__)
+
+
+class Qwen3MoeSparseMoeBlock(nn.Module):
+    def __init__(
+        self,
+        layer_id: int,
+        config: Qwen3MoeConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.layer_id = layer_id
+        if self.tp_size > config.num_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.num_experts}."
+            )
+
+        self.experts = get_moe_impl_class()(
+            num_experts=config.num_experts
+            + global_server_args_dict["ep_num_redundant_experts"],
+            top_k=config.num_experts_per_tok,
+            layer_id=layer_id,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            renormalize=config.norm_topk_prob,
+            quant_config=quant_config,
+            prefix=add_prefix("experts", prefix),
+            **(
+                dict(deepep_mode=DeepEPMode[global_server_args_dict["deepep_mode"]])
+                if global_server_args_dict["enable_deepep_moe"]
+                else {}
+            ),
+        )
+
+        self.gate = ReplicatedLinear(
+            config.hidden_size,
+            config.num_experts,
+            bias=False,
+            quant_config=None,
+            prefix=add_prefix("gate", prefix),
+        )
+
+        if global_server_args_dict["enable_deepep_moe"]:
+            # TODO: we will support tp < ep in the future
+            self.ep_size = get_tensor_model_parallel_world_size()
+            self.num_experts = (
+                config.num_experts + global_server_args_dict["ep_num_redundant_experts"]
+            )
+            self.top_k = config.num_experts_per_tok
+            self.renormalize = config.norm_topk_prob
+
+            self.deepep_dispatcher = MaybeTboDeepEPDispatcher(
+                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=DeepEPMode[global_server_args_dict["deepep_mode"]],
+                async_finish=True,  # TODO
+                return_recv_hook=True,
+            )
+
+    def forward(
+        self, hidden_states: torch.Tensor, forward_batch: Optional[ForwardBatch] = None
+    ) -> torch.Tensor:
+
+        if not global_server_args_dict["enable_deepep_moe"]:
+            return self.forward_normal(hidden_states)
+        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_normal(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.gate(hidden_states)
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states, router_logits=router_logits
+        )
+        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 forward_deepep(
+        self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
+    ) -> torch.Tensor:
+        forward_mode = forward_batch.forward_mode
+        if is_non_idle_and_non_empty(forward_mode, hidden_states):
+            # router_logits: (num_tokens, n_experts)
+            router_logits, _ = self.gate(hidden_states)
+
+            topk_weights, topk_idx = select_experts(
+                hidden_states=hidden_states,
+                router_logits=router_logits,
+                top_k=self.top_k,
+                use_grouped_topk=False,
+                renormalize=self.renormalize,
+                num_token_non_padded=forward_batch.num_token_non_padded,
+                expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
+                    layer_id=self.layer_id,
+                ),
+            )
+        else:
+            topk_idx = torch.full(
+                (0, self.top_k), -1, dtype=torch.int, device=hidden_states.device
+            )
+            topk_weights = torch.empty(
+                (0, self.top_k), dtype=torch.float32, device=hidden_states.device
+            )
+        if self.ep_size > 1:
+            # TODO(ch-wan): allow users to set num_max_dispatch_tokens_per_rank value
+            (
+                hidden_states,
+                topk_idx,
+                topk_weights,
+                reorder_topk_ids,
+                num_recv_tokens_per_expert,
+                seg_indptr,
+                masked_m,
+                expected_m,
+            ) = self.deepep_dispatcher.dispatch(
+                hidden_states=hidden_states,
+                topk_idx=topk_idx,
+                topk_weights=topk_weights,
+                forward_mode=forward_mode,
+            )
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states,
+            topk_idx=topk_idx,
+            topk_weights=topk_weights,
+            reorder_topk_ids=reorder_topk_ids,
+            seg_indptr=seg_indptr,
+            masked_m=masked_m,
+            expected_m=expected_m,
+            num_recv_tokens_per_expert=num_recv_tokens_per_expert,
+            forward_mode=forward_mode,
+        )
+        if self.ep_size > 1:
+            final_hidden_states = self.deepep_dispatcher.combine(
+                hidden_states=final_hidden_states,
+                topk_idx=topk_idx,
+                topk_weights=topk_weights,
+                forward_mode=forward_mode,
+            )
+        return final_hidden_states
+
+    def op_gate(self, state):
+        if is_non_idle_and_non_empty(
+            state.forward_batch.forward_mode, state.hidden_states_mlp_input
+        ):
+            # router_logits: (num_tokens, n_experts)
+            state.router_logits, _ = self.gate(state.hidden_states_mlp_input)
+        else:
+            state.router_logits = None
+
+    def op_select_experts(self, state):
+        router_logits = state.pop("router_logits")
+        hidden_states = state.hidden_states_mlp_input
+        if router_logits is not None:
+            with get_global_expert_distribution_recorder().with_current_layer(
+                self.layer_id
+            ):
+                state.topk_weights_local, state.topk_idx_local = select_experts(
+                    hidden_states=hidden_states,
+                    router_logits=router_logits,
+                    top_k=self.top_k,
+                    use_grouped_topk=False,
+                    renormalize=self.renormalize,
+                    num_token_non_padded=state.forward_batch.num_token_non_padded,
+                    expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
+                        layer_id=self.layer_id,
+                    ),
+                )
+        else:
+            state.topk_idx_local = torch.full(
+                (0, self.top_k), -1, dtype=torch.int, device=hidden_states.device
+            )
+            state.topk_weights_local = torch.empty(
+                (0, self.top_k), dtype=torch.float32, device=hidden_states.device
+            )
+
+    def op_dispatch_a(self, state):
+        if self.ep_size > 1:
+            # TODO(ch-wan): allow users to set num_max_dispatch_tokens_per_rank value
+            self.deepep_dispatcher.dispatch_a(
+                hidden_states=state.pop("hidden_states_mlp_input"),
+                topk_idx=state.pop("topk_idx_local"),
+                topk_weights=state.pop("topk_weights_local"),
+                forward_mode=state.forward_batch.forward_mode,
+                tbo_subbatch_index=state.get("tbo_subbatch_index"),
+            )
+
+    def op_dispatch_b(self, state):
+        if self.ep_size > 1:
+            with get_global_expert_distribution_recorder().with_current_layer(
+                self.layer_id
+            ):
+                (
+                    state.hidden_states_experts_input,
+                    state.topk_idx_dispatched,
+                    state.topk_weights_dispatched,
+                    state.reorder_topk_ids,
+                    state.num_recv_tokens_per_expert,
+                    state.seg_indptr,
+                    state.masked_m,
+                    state.expected_m,
+                ) = self.deepep_dispatcher.dispatch_b(
+                    tbo_subbatch_index=state.get("tbo_subbatch_index"),
+                )
+
+    def op_experts(self, state):
+        state.hidden_states_experts_output = self.experts(
+            hidden_states=state.pop("hidden_states_experts_input"),
+            topk_idx=state.topk_idx_dispatched,
+            topk_weights=state.topk_weights_dispatched,
+            reorder_topk_ids=state.pop("reorder_topk_ids"),
+            seg_indptr=state.pop("seg_indptr"),
+            masked_m=state.pop("masked_m"),
+            expected_m=state.pop("expected_m"),
+            num_recv_tokens_per_expert=state.pop("num_recv_tokens_per_expert"),
+            forward_mode=state.forward_batch.forward_mode,
+        )
+
+    def op_combine_a(self, state):
+        if self.ep_size > 1:
+            self.deepep_dispatcher.combine_a(
+                hidden_states=state.pop("hidden_states_experts_output"),
+                topk_idx=state.pop("topk_idx_dispatched"),
+                topk_weights=state.pop("topk_weights_dispatched"),
+                forward_mode=state.forward_batch.forward_mode,
+                tbo_subbatch_index=state.get("tbo_subbatch_index"),
+            )
+
+    def op_combine_b(self, state):
+        if self.ep_size > 1:
+            state.hidden_states_after_combine = self.deepep_dispatcher.combine_b(
+                tbo_subbatch_index=state.get("tbo_subbatch_index"),
+            )
+
+    def op_output(self, state):
+        state.hidden_states_mlp_output = state.pop("hidden_states_after_combine")
+
+
+class Qwen3MoeAttention(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        layer_id: int = 0,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        head_dim: Optional[int] = None,
+        rms_norm_eps: float = 1e-06,
+        attention_bias: bool = False,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+
+        attn_tp_rank = get_attention_tp_rank()
+        attn_tp_size = get_attention_tp_size()
+
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % attn_tp_size == 0
+        self.num_heads = self.total_num_heads // attn_tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= attn_tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % attn_tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert attn_tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // attn_tp_size)
+        self.head_dim = head_dim or hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+        self.tp_rank = get_tensor_model_parallel_rank()
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=attention_bias,
+            quant_config=quant_config,
+            tp_rank=attn_tp_rank,
+            tp_size=attn_tp_size,
+            prefix=add_prefix("qkv_proj", prefix),
+        )
+
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=attention_bias,
+            quant_config=quant_config,
+            tp_rank=attn_tp_rank,
+            tp_size=attn_tp_size,
+            reduce_results=False,
+            prefix=add_prefix("o_proj", prefix),
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+        self.attn = RadixAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            layer_id=layer_id,
+            prefix=add_prefix("attn", prefix),
+        )
+
+        self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
+        self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
+
+    def _apply_qk_norm(
+        self, q: torch.Tensor, k: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        q_by_head = q.reshape(-1, self.head_dim)
+        q_by_head = self.q_norm(q_by_head)
+        q = q_by_head.view(q.shape)
+        k_by_head = k.reshape(-1, self.head_dim)
+        k_by_head = self.k_norm(k_by_head)
+        k = k_by_head.view(k.shape)
+        return q, k
+
+    def op_prepare(self, state):
+        state.attn_intermediate_state = self.forward_prepare(
+            positions=state.positions,
+            hidden_states=state.pop("hidden_states_after_comm_pre_attn"),
+            forward_batch=state.forward_batch,
+        )
+
+    def op_core(self, state):
+        state.hidden_states_after_attn = self.forward_core(
+            state.pop("attn_intermediate_state")
+        )
+
+    def forward_prepare(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ):
+        if hidden_states.shape[0] == 0:
+            return hidden_states, forward_batch, None
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self._apply_qk_norm(q, k)
+        q, k = self.rotary_emb(positions, q, k)
+        inner_state = q, k, v, forward_batch
+        return None, forward_batch, inner_state
+
+    def forward_core(self, intermediate_state):
+        hidden_states, forward_batch, inner_state = intermediate_state
+        if inner_state is None:
+            return hidden_states
+        attn_output = self.attn(*inner_state)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        s = self.forward_prepare(
+            positions=positions,
+            hidden_states=hidden_states,
+            forward_batch=forward_batch,
+        )
+        return self.forward_core(s)
+
+
+class Qwen3MoeDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: Qwen3MoeConfig,
+        layer_id: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
+        head_dim = getattr(
+            config, "head_dim", config.hidden_size // config.num_attention_heads
+        )
+        rms_norm_eps = config.rms_norm_eps
+        attention_bias = config.attention_bias
+        self.self_attn = Qwen3MoeAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            layer_id=layer_id,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            head_dim=head_dim,
+            rms_norm_eps=rms_norm_eps,
+            attention_bias=attention_bias,
+            quant_config=quant_config,
+            prefix=add_prefix("self_attn", prefix),
+        )
+
+        self.layer_id = layer_id
+
+        self.attn_tp_size = get_attention_tp_size()
+        self.attn_tp_rank = get_attention_tp_rank()
+        self.local_dp_size = get_local_attention_dp_size()
+
+        # Qwen3MoE all layers are sparse and have no nextn now
+        self.is_layer_sparse = True
+        is_previous_layer_sparse = True
+
+        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,
+        )
+
+        if self.is_layer_sparse:
+            self.mlp = Qwen3MoeSparseMoeBlock(
+                layer_id=self.layer_id,
+                config=config,
+                quant_config=quant_config,
+                prefix=add_prefix("mlp", prefix),
+            )
+        else:
+            self.mlp = Qwen3MoeMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                prefix=add_prefix("mlp", prefix),
+            )
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+        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],
+    ) -> Tuple[torch.Tensor, 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,
+            )
+
+        hidden_states, residual = self.layer_communicator.prepare_mlp(
+            hidden_states, residual, forward_batch
+        )
+
+        hidden_states = self.mlp(hidden_states, forward_batch)
+
+        hidden_states, residual = self.layer_communicator.postprocess_layer(
+            hidden_states, residual, forward_batch
+        )
+
+        return hidden_states, residual
+
+    def op_comm_prepare_attn(
+        self,
+        state,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+        residual: Optional[torch.Tensor],
+        tbo_subbatch_index: Optional[int] = None,
+    ):
+        state.hidden_states_after_comm_pre_attn, state.residual_after_input_ln = (
+            self.layer_communicator.prepare_attn(hidden_states, residual, forward_batch)
+        )
+        state.update(
+            dict(
+                forward_batch=forward_batch,
+                positions=positions,
+                tbo_subbatch_index=tbo_subbatch_index,
+            )
+        )
+
+    def op_comm_prepare_mlp(self, state):
+        state.hidden_states_mlp_input, state.residual_after_comm_pre_mlp = (
+            self.layer_communicator.prepare_mlp(
+                state.pop("hidden_states_after_attn"),
+                state.pop("residual_after_input_ln"),
+                state.forward_batch,
+            )
+        )
+
+    def op_mlp(self, state):
+        hidden_states = state.pop("hidden_states_mlp_input")
+        state.hidden_states_mlp_output = self.mlp(
+            hidden_states, state.forward_batch.forward_mode
+        )
+
+    def op_comm_postprocess_layer(self, state):
+        hidden_states, residual = self.layer_communicator.postprocess_layer(
+            state.pop("hidden_states_mlp_output"),
+            state.pop("residual_after_comm_pre_mlp"),
+            state.forward_batch,
+        )
+
+        output = dict(
+            positions=state.positions,
+            hidden_states=hidden_states,
+            residual=residual,
+            forward_batch=state.forward_batch,
+            tbo_subbatch_index=state.tbo_subbatch_index,
+        )
+
+        state.clear(
+            expect_keys={
+                "positions",
+                "forward_batch",
+                "tbo_subbatch_index",
+            }
+        )
+        return output
+
+
+class Qwen3MoeModel(Qwen2MoeModel):
+    def __init__(
+        self,
+        config: Qwen3MoeConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__(
+            config=config,
+            quant_config=quant_config,
+            prefix=prefix,
+            decoder_layer_type=Qwen3MoeDecoderLayer,
+        )
+        
+        # For EAGLE3 support
+        self.layers_to_capture = []
+    
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+        pp_proxy_tensors: Optional[PPProxyTensors] = None,
+    ) -> Union[torch.Tensor, PPProxyTensors]:
+        if self.pp_group.is_first_rank:
+            if input_embeds is None:
+                hidden_states = self.embed_tokens(input_ids)
+            else:
+                hidden_states = input_embeds
+            residual = None
+        else:
+            assert pp_proxy_tensors is not None
+            hidden_states = pp_proxy_tensors["hidden_states"]
+            residual = pp_proxy_tensors["residual"]
+
+        # For EAGLE3 support - collect auxiliary hidden states
+        aux_hidden_states = []
+        
+        if forward_batch.can_run_tbo:
+            hidden_states, residual = model_forward_maybe_tbo(
+                layers=self.layers,
+                enable_tbo=True,
+                input_data_scatter_mode=ScatterMode.model_input_output(),
+                positions=positions,
+                forward_batch=forward_batch,
+                hidden_states=hidden_states,
+                residual=residual,
+            )
+        else:
+            for i in range(self.start_layer, self.end_layer):
+                # EAGLE3 support: capture hidden states from specified layers
+                if i in self.layers_to_capture:
+                    aux_hidden_states.append(hidden_states + residual)
+                    
+                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 hidden_states.shape[0] != 0:
+                if residual is None:
+                    hidden_states = self.norm(hidden_states)
+                else:
+                    hidden_states, _ = self.norm(hidden_states, residual)
+                    
+        # Return aux_hidden_states if available for EAGLE3
+        if len(aux_hidden_states) == 0:
+            return hidden_states
+        return hidden_states, aux_hidden_states
+
+
+class Qwen3MoeForCausalLM(nn.Module):
+    fall_back_to_pt_during_load = False
+
+    def __init__(
+        self,
+        config: Qwen3MoeConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.pp_group = get_pp_group()
+        self.config = config
+        self.quant_config = quant_config
+        self.model = Qwen3MoeModel(
+            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)
+        
+        # For EAGLE3 support
+        self.capture_aux_hidden_states = False
+
+    @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,
+        )
+        
+        aux_hidden_states = None
+        if self.capture_aux_hidden_states:
+            hidden_states, aux_hidden_states = hidden_states
+
+        if self.pp_group.is_last_rank:
+            return self.logits_processor(
+                input_ids, hidden_states, self.lm_head, forward_batch, aux_hidden_states
+            )
+        else:
+            return hidden_states
+
+    @property
+    def start_layer(self):
+        return self.model.start_layer
+
+    @property
+    def end_layer(self):
+        return self.model.end_layer
+    
+    def get_embed_and_head(self):
+        return self.model.embed_tokens.weight, self.lm_head.weight
+        
+    def set_eagle3_layers_to_capture(self):
+        if not self.pp_group.is_last_rank:
+            return
+
+        self.capture_aux_hidden_states = True
+        num_layers = self.config.num_hidden_layers
+        self.model.layers_to_capture = [2, num_layers // 2, num_layers - 3]
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        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.num_experts,
+        )
+
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+            layer_id = get_layer_id(name)
+            if (
+                layer_id is not None
+                and hasattr(self.model, "start_layer")
+                and (
+                    layer_id < self.model.start_layer
+                    or layer_id >= self.model.end_layer
+                )
+            ):
+                continue
+
+            if "rotary_emb.inv_freq" in name:
+                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:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(
+                        param,
+                        loaded_weight,
+                        name,
+                        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
+
+                    if name in params_dict.keys():
+                        param = params_dict[name]
+                        weight_loader = getattr(
+                            param, "weight_loader", default_weight_loader
+                        )
+                        weight_loader(param, loaded_weight)
+                    else:
+                        logger.warning(f"Parameter {name} not found in params_dict")
+
+        # TODO mimic deepseek
+        self.routed_experts_weights_of_layer = {
+            layer_id: self.model.layers[layer_id].mlp.get_moe_weights()
+            for layer_id in range(self.start_layer, self.end_layer)
+            if isinstance(self.model.layers[layer_id].mlp, Qwen3MoeSparseMoeBlock)
+        }
+
+    @classmethod
+    def get_model_config_for_expert_location(cls, config):
+        return ModelConfigForExpertLocation(
+            num_layers=config.num_hidden_layers,
+            num_logical_experts=config.num_experts,
+            num_groups=None,
+        )
+
+
+EntryClass = Qwen3MoeForCausalLM