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[Spec Decode] Support EAGLE for Qwen3 MoE #26241

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[Spec Decode] Support EAGLE for Qwen3 MoE #26241

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28 changes: 25 additions & 3 deletions vllm/model_executor/models/qwen3_moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -64,7 +64,7 @@
from vllm.model_executor.models.utils import sequence_parallel_chunk
from vllm.sequence import IntermediateTensors

from .interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP
from .interfaces import MixtureOfExperts, SupportsEagle3, SupportsLoRA, SupportsPP
from .utils import (
AutoWeightsLoader,
PPMissingLayer,
Expand Down Expand Up @@ -422,6 +422,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
["hidden_states", "residual"], config.hidden_size
)
self.aux_hidden_state_layers = tuple[int, ...]()

def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.embed_tokens(input_ids)
Expand All @@ -443,13 +444,25 @@ def forward(
assert intermediate_tensors is not None
hidden_states = intermediate_tensors["hidden_states"]
residual = intermediate_tensors["residual"]
for layer in islice(self.layers, self.start_layer, self.end_layer):

aux_hidden_states = []
for idx, layer in enumerate(
islice(self.layers, self.start_layer, self.end_layer)
):
if idx in self.aux_hidden_state_layers:
aux_hidden_states.append(hidden_states + residual)
hidden_states, residual = layer(positions, hidden_states, residual)

if not get_pp_group().is_last_rank:
return IntermediateTensors(
{"hidden_states": hidden_states, "residual": residual}
)

hidden_states, _ = self.norm(hidden_states, residual)

if len(aux_hidden_states) > 0:
return hidden_states, aux_hidden_states

return hidden_states

def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
Expand Down Expand Up @@ -606,7 +619,9 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
return loaded_params


class Qwen3MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA, MixtureOfExperts):
class Qwen3MoeForCausalLM(
nn.Module, SupportsPP, SupportsLoRA, SupportsEagle3, MixtureOfExperts
):
packed_modules_mapping = {
"qkv_proj": [
"q_proj",
Expand Down Expand Up @@ -669,6 +684,13 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
self.num_routed_experts = example_layer.n_routed_experts
self.num_redundant_experts = example_layer.n_redundant_experts

def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
self.model.aux_hidden_state_layers = layers

def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
num_layers = len(self.model.layers)
return (2, num_layers // 2, num_layers - 3)

def set_eplb_state(
self,
expert_load_view: torch.Tensor,
Expand Down