[Multimodal][Speculative Decoding]Eagle Eagle3 mm support, enablement on qwen2.5vl

tests/models/registry.py

@@ -651,6 +651,9 @@ def check_available_online(
     "MiMoMTPModel": _HfExamplesInfo("XiaomiMiMo/MiMo-7B-RL",
                                     trust_remote_code=True,
                                     speculative_model="XiaomiMiMo/MiMo-7B-RL"),
+    "Eagle3Qwen2_5vlForCausalLM": _HfExamplesInfo(
+        "Qwen/Qwen2.5-VL-7B-Instruct",
+        speculative_model="Rayzl/qwen2.5-vl-7b-eagle3-sgl"),
     "Qwen3NextMTP": _HfExamplesInfo("Qwen/Qwen3-Next-80B-A3B-Instruct",
                                      min_transformers_version="4.56.3"),
 }

tests/v1/e2e/test_spec_decode.py

@@ -127,6 +127,8 @@ def test_ngram_correctness(
 
 @pytest.mark.parametrize(["model_setup", "mm_enabled"], [
     (("eagle3", "Qwen/Qwen3-8B", "AngelSlim/Qwen3-8B_eagle3", 1), False),
+    (("eagle3", "Qwen/Qwen2.5-VL-7B-Instruct",
+      "Rayzl/qwen2.5-vl-7b-eagle3-sgl", 1), False),
     (("eagle", "meta-llama/Llama-3.1-8B-Instruct",
       "yuhuili/EAGLE-LLaMA3.1-Instruct-8B", 1), False),
     (("eagle3", "meta-llama/Llama-3.1-8B-Instruct",
@@ -145,9 +147,9 @@ def test_ngram_correctness(
       "eagle618/eagle-deepseek-v3-random", 1), False),
 ],
                          ids=[
-                             "qwen3_eagle3", "llama3_eagle", "llama3_eagle3",
-                             "llama4_eagle", "llama4_eagle_mm",
-                             "deepseek_eagle"
+                             "qwen3_eagle3", "qwen2_5_vl_eagle3",
+                             "llama3_eagle", "llama3_eagle3", "llama4_eagle",
+                             "llama4_eagle_mm", "deepseek_eagle"
                          ])
 @pytest.mark.parametrize("attn_backend",
                          get_attn_backend_list_based_on_platform())

vllm/benchmarks/datasets.py

@@ -1450,6 +1450,13 @@ def get_samples(args, tokenizer) -> list[SampleRequest]:
         ):
             dataset_class = MLPerfDataset
             args.hf_split = "train"
+        elif (
+            args.dataset_path in MMStarDataset.SUPPORTED_DATASET_PATHS
+            or args.hf_name in MMStarDataset.SUPPORTED_DATASET_PATHS
+        ):
+            dataset_class = MMStarDataset
+            args.hf_split = "val"
+            args.hf_subset = None
         else:
             supported_datasets = set([
                 dataset_name for cls in HuggingFaceDataset.__subclasses__()
@@ -2721,3 +2728,76 @@ def _generate_exact_length_tokens(target_length: int) -> list[int]:
 
         random.shuffle(requests)
         return requests
+
+
+# -----------------------------------------------------------------------------
+# MMStar Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class MMStarDataset(HuggingFaceDataset):
+    """
+    Lin-Chen/MMStar: https://huggingface.co/datasets/Lin-Chen/MMStar
+    refer to: https://github.com/sgl-project/SpecForge/pull/106
+    """
+    DEFAULT_OUTPUT_LEN = 128
+    SUPPORTED_DATASET_PATHS = {"Lin-Chen/MMStar"}
+    IS_MULTIMODAL = True
+
+    def sample(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        output_len: Optional[int] = None,
+        enable_multimodal_chat: bool = False,
+        request_id_prefix: str = "",
+        no_oversample: bool = False,
+        **kwargs,
+    ) -> list[SampleRequest]:
+        # If --hf-output-len is not set, use the default output length.
+        output_len = (output_len
+                      if output_len is not None else self.DEFAULT_OUTPUT_LEN)
+        sampled_requests: list[SampleRequest] = []
+
+        for ind, item in enumerate(self.data):
+            if len(sampled_requests) >= num_requests:
+                break
+            # Split the question text from options
+            # (keep only the part before "Options:").
+            full_q: str = item.get("question", "")
+            question_text = full_q.split("Options:", 1)[0].strip()
+
+            # Multimodal image content.
+            mm_content = process_image(item["image"])
+
+            # Compute prompt token length (note: this is plain text length
+            # if enable_multimodal_chat is False).
+            prompt_len = len(tokenizer(question_text).input_ids)
+
+            if enable_multimodal_chat:
+                # If multimodal content should be embedded in the chat message,
+                # convert to [{"role":"user","content":[...]}]
+                prompt = self.apply_multimodal_chat_transformation(
+                    question_text, mm_content
+                )
+                mm_for_request = None  # Already embedded in chat content.
+            else:
+                # Default: prompt is plain text,
+                # image is in mm_content for the bench to assemble.
+                prompt = question_text
+                mm_for_request = mm_content
+
+            sampled_requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                    multi_modal_data=mm_for_request,
+                    request_id=request_id_prefix + str(ind),
+                )
+            )
+
+        self.maybe_oversample_requests(
+            sampled_requests, num_requests, request_id_prefix, no_oversample
+        )
+        return sampled_requests

vllm/model_executor/models/llama_eagle3.py

@@ -8,7 +8,6 @@
 import torch.nn as nn
 from transformers import LlamaConfig
 
-from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
 from vllm.logger import init_logger
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -19,6 +18,7 @@
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.interfaces import MultiModalEmbeddings
 from vllm.model_executor.models.llama import (LlamaDecoderLayer,
                                               LlamaForCausalLM)
 
@@ -102,7 +102,6 @@ def forward(
         return hidden_states, residual
 
 
-@support_torch_compile
 class LlamaModel(nn.Module):
 
     def __init__(
@@ -145,13 +144,21 @@ def __init__(
             eps=self.config.rms_norm_eps,
         )
 
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+    ) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
     def forward(
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
+        input_embeds: Optional[torch.Tensor] = None,
     ) -> tuple[torch.Tensor, torch.Tensor]:
-        input_embeds = self.embed_tokens(input_ids)
+        if input_embeds is None:
+            input_embeds = self.get_input_embeddings(input_ids)
         assert hidden_states.shape[-1] == input_embeds.shape[-1]
 
         residual = None
@@ -239,11 +246,7 @@ def forward(
         hidden_states: torch.Tensor,
         inputs_embeds: Optional[torch.Tensor] = None,
     ) -> tuple[torch.Tensor, torch.Tensor]:
-        if inputs_embeds is not None:
-            raise NotImplementedError(
-                f"{type(self).__name__} does not support multimodal inputs yet."
-            )
-        return self.model(input_ids, positions, hidden_states)
+        return self.model(input_ids, positions, hidden_states, inputs_embeds)
 
     def compute_logits(
         self,
@@ -299,3 +302,11 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
             skip_substrs=skip_substrs,
         )
         loader.load_weights(model_weights.items())
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.model.get_input_embeddings(input_ids)
+        return inputs_embeds

vllm/model_executor/models/qwen2_5_vl.py

@@ -68,7 +68,7 @@
 from vllm.utils import is_pin_memory_available
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
-from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+from .interfaces import (MultiModalEmbeddings, SupportsEagle3, SupportsLoRA,
                          SupportsMultiModal, SupportsMultiModalPruning,
                          SupportsPP, SupportsQuant)
 from .qwen2_vl import Qwen2VLDummyInputsBuilder as Qwen2_5_VLDummyInputsBuilder
@@ -965,7 +965,7 @@ def get_replacement_qwen2vl(item_idx: int, modality: str):
     dummy_inputs=Qwen2_5_VLDummyInputsBuilder)
 class Qwen2_5_VLForConditionalGeneration(nn.Module, SupportsMultiModal,
                                          SupportsLoRA, SupportsPP,
-                                         SupportsQuant,
+                                         SupportsQuant, SupportsEagle3,
                                          SupportsMultiModalPruning):
 
     packed_modules_mapping = {
@@ -1028,6 +1028,13 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors)
 
+    def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
+        self.language_model.model.aux_hidden_state_layers = layers
+
+    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
+        num_layers = len(self.language_model.model.layers)
+        return (2, num_layers // 2, num_layers - 3)
+
     def _validate_and_reshape_mm_tensor(self, mm_input: object,
                                         name: str) -> torch.Tensor:
         if not isinstance(mm_input, (torch.Tensor, list)):

vllm/model_executor/models/registry.py

@@ -286,6 +286,7 @@
     "EagleMiniCPMForCausalLM": ("minicpm_eagle", "EagleMiniCPMForCausalLM"),
     "Eagle3LlamaForCausalLM": ("llama_eagle3", "Eagle3LlamaForCausalLM"),
     "LlamaForCausalLMEagle3": ("llama_eagle3", "Eagle3LlamaForCausalLM"),
+    "Eagle3Qwen2_5vlForCausalLM": ("llama_eagle3", "Eagle3LlamaForCausalLM"),
     "EagleDeepSeekMTPModel": ("deepseek_eagle", "EagleDeepseekV3ForCausalLM"),
     "DeepSeekMTPModel": ("deepseek_mtp", "DeepSeekMTP"),
     "ErnieMTPModel": ("ernie_mtp", "ErnieMTP"),