From cb27cef583aad905fedc2cb50062e972b5953a76 Mon Sep 17 00:00:00 2001 From: seven-mile Date: Sun, 5 Oct 2025 04:34:18 +0000 Subject: [PATCH] [SpecDecode] Support EAGLE for Qwen3 MoE Signed-off-by: seven-mile --- vllm/model_executor/models/qwen3_moe.py | 28 ++++++++++++++++++++++--- 1 file changed, 25 insertions(+), 3 deletions(-) diff --git a/vllm/model_executor/models/qwen3_moe.py b/vllm/model_executor/models/qwen3_moe.py index 34b5af846493..748de732af96 100644 --- a/vllm/model_executor/models/qwen3_moe.py +++ b/vllm/model_executor/models/qwen3_moe.py @@ -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, @@ -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) @@ -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]]: @@ -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", @@ -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,