MiniMax-M2: add Eagle3 speculative decoding support (#37512)

Signed-off-by: liuchenbing <chenliumail@163.com>
Signed-off-by: liucb <liuchengbao_work@163.com>
Co-authored-by: liuchenbing <chenliumail@163.com>

tests/models/registry.py

@@ -1246,6 +1246,12 @@ _SPECULATIVE_DECODING_EXAMPLE_MODELS = {
         use_original_num_layers=True,
         max_model_len=10240,
     ),
+    "Eagle3MiniMaxM2ForCausalLM": _HfExamplesInfo(
+        "MiniMaxAI/MiniMax-M2",
+        trust_remote_code=True,
+        speculative_model="yuhuili/EAGLE3-LLaMA3.1-Instruct-8B",
+        tokenizer="MiniMaxAI/MiniMax-M2",
+    ),
     "EagleMistralLarge3ForCausalLM": _HfExamplesInfo(
         "mistralai/Mistral-Large-3-675B-Instruct-2512",
         speculative_model="mistralai/Mistral-Large-3-675B-Instruct-2512-Eagle",

vllm/config/speculative.py

@@ -817,6 +817,7 @@ class SpeculativeConfig:
             "deepseek_v3",
             "kimi_k2",
             "kimi_k25",
+            "minimax_m2",
         ]
         if (
             self.method in ("eagle3", "extract_hidden_states", "dflash")

vllm/model_executor/models/minimax_m2.py

@@ -24,6 +24,7 @@
 """Inference-only MiniMaxM2 model."""
 
 from collections.abc import Iterable
+from itertools import islice
 from typing import Any
 
 import torch
@@ -59,7 +60,7 @@ from vllm.model_executor.model_loader.weight_utils import (
 )
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import SupportsLoRA, SupportsPP
+from .interfaces import EagleModelMixin, SupportsEagle3, SupportsLoRA, SupportsPP
 from .utils import (
     AutoWeightsLoader,
     PPMissingLayer,
@@ -313,7 +314,7 @@ class MiniMaxM2DecoderLayer(nn.Module):
 
 
 @support_torch_compile
-class MiniMaxM2Model(nn.Module):
+class MiniMaxM2Model(nn.Module, EagleModelMixin):
     fall_back_to_pt_during_load = False
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
@@ -366,7 +367,7 @@ class MiniMaxM2Model(nn.Module):
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None,
         inputs_embeds: torch.Tensor | None = None,
-    ) -> torch.Tensor | IntermediateTensors:
+    ) -> torch.Tensor | IntermediateTensors | tuple[torch.Tensor, list[torch.Tensor]]:
         if get_pp_group().is_first_rank:
             if inputs_embeds is not None:
                 hidden_states = inputs_embeds
@@ -378,14 +379,24 @@ class MiniMaxM2Model(nn.Module):
             hidden_states = intermediate_tensors["hidden_states"]
             residual = intermediate_tensors["residual"]
 
-        for layer in self.layers[self.start_layer : self.end_layer]:
+        aux_hidden_states = self._maybe_add_hidden_state([], 0, hidden_states, residual)
+        for idx, layer in enumerate(
+            islice(self.layers, self.start_layer, self.end_layer)
+        ):
             hidden_states, residual = layer(positions, hidden_states, residual)
+            self._maybe_add_hidden_state(
+                aux_hidden_states, idx + 1, 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]]:
@@ -496,7 +507,7 @@ class MiniMaxM2Model(nn.Module):
         return loaded_params
 
 
-class MiniMaxM2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+class MiniMaxM2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",

vllm/model_executor/models/registry.py

@@ -554,6 +554,7 @@ _SPECULATIVE_DECODING_MODELS = {
     "EagleMiniCPMForCausalLM": ("minicpm_eagle", "EagleMiniCPMForCausalLM"),
     "DFlashDraftModel": ("qwen3_dflash", "DFlashQwen3ForCausalLM"),
     "Eagle3LlamaForCausalLM": ("llama_eagle3", "Eagle3LlamaForCausalLM"),
+    "Eagle3MiniMaxM2ForCausalLM": ("llama_eagle3", "Eagle3LlamaForCausalLM"),
     "LlamaForCausalLMEagle3": ("llama_eagle3", "Eagle3LlamaForCausalLM"),
     "Eagle3Qwen2_5vlForCausalLM": ("llama_eagle3", "Eagle3LlamaForCausalLM"),
     "Eagle3Qwen3vlForCausalLM": ("llama_eagle3", "Eagle3LlamaForCausalLM"),