[Bugfix][Speculative Decoding] Fix Eagle3 quantization config issue (#25883)

Signed-off-by: Rahul Tuli <rtuli@redhat.com>

tests/speculative_decoding/speculators/test_eagle3.py

@@ -14,6 +14,9 @@ from vllm.model_executor.models.interfaces import supports_eagle3
     pytest.param(
         "nm-testing/Speculator-Qwen3-8B-Eagle3-converted-071-quantized",
         id="qwen3-eagle3-speculator"),
+    pytest.param(
+        "nm-testing/Speculator-Qwen3-8B-Eagle3-converted-071-quantized-w4a16",
+        id="qwen3-eagle3-speculator-w4a16-verifier"),
 ])
 def test_eagle3_speculators_model(vllm_runner, example_prompts, model_path,
                                   monkeypatch):

vllm/model_executor/models/llama.py

@@ -248,7 +248,7 @@ class LlamaDecoderLayer(nn.Module):
 
         config = config or vllm_config.model_config.hf_config
         cache_config = vllm_config.cache_config
-        quant_config = vllm_config.quant_config
+        quant_config = self.get_quant_config(vllm_config)
 
         self.hidden_size = config.hidden_size
         rope_theta = getattr(config, "rope_theta", 10000)
@@ -328,6 +328,11 @@ class LlamaDecoderLayer(nn.Module):
         hidden_states = self.mlp(hidden_states)
         return hidden_states, residual
 
+    def get_quant_config(
+            self, vllm_config: VllmConfig) -> Optional[QuantizationConfig]:
+        """Get quantization config for this layer. Override in subclasses."""
+        return vllm_config.quant_config
+
 
 @support_torch_compile
 class LlamaModel(nn.Module):

vllm/model_executor/models/llama_eagle3.py

@@ -13,6 +13,8 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import QKVParallelLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
 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
@@ -33,7 +35,7 @@ class LlamaDecoderLayer(LlamaDecoderLayer):
         super().__init__(vllm_config, prefix=prefix, config=config)
 
         config = config or vllm_config.model_config.hf_config
-        quant_config = vllm_config.quant_config
+        quant_config = self.get_quant_config(vllm_config)
 
         # override qkv
         self.self_attn.qkv_proj = QKVParallelLinear(
@@ -53,6 +55,16 @@ class LlamaDecoderLayer(LlamaDecoderLayer):
         else:
             self._residual_norm = self._norm_after_residual
 
+    def get_quant_config(
+            self, vllm_config: VllmConfig) -> Optional[QuantizationConfig]:
+        """Use drafter's quantization config instead of verifier's."""
+        draft_model_config = vllm_config.speculative_config.draft_model_config
+        draft_load_config = vllm_config.load_config
+
+        return VllmConfig.get_quantization_config(
+            draft_model_config,
+            draft_load_config) if draft_model_config else None
+
     def _norm_before_residual(
             self,
             hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: