Fix Bug #1: Read tau_d from target temperature, not draft_temperature

CRITICAL FIX: tau_d was reading draft_temperature (0.05) instead of
target temperature from sampling_metadata (1.0).

This caused:
- tau_q = 0.05 + 0.3 = 0.35 (before)
- Logit gap = 10/0.35 = 28.6 → exp(-28.6) ≈ 0 (underflow!)
- q collapses to 0.98-1.0

After fix:
- tau_d = 1.0 (from sampling_metadata.temperature)
- tau_q = 1.0 + 0.3 = 1.3
- Logit gap = 10/1.3 = 7.7 → exp(-7.7) = 0.00045 (survives!)
- q should be in [0.5, 0.8] range

Changes:
- propose(): Store sampling_metadata as self._current_sampling_metadata
- _sample_draft_tokens(): Read tau_d from sampling_metadata, not opt_config

Author: yuz207

vllm/v1/spec_decode/eagle.py

@@ -239,7 +239,10 @@ def _sample_draft_tokens(
             x = x - x.amax(dim=-1, keepdim=True)
 
             # --- temperature for drafter q ---
-            tau_d = float(getattr(self.opt_config, "draft_temperature", 1.0) or 1.0)
+            # Read from TARGET temperature (not draft_temperature)
+            tau_d = 1.0
+            if hasattr(self, '_current_sampling_metadata') and self._current_sampling_metadata is not None:
+                tau_d = float(getattr(self._current_sampling_metadata, 'temperature', 1.0))
 
             tau_q = tau_d + float(getattr(self.opt_config, "draft_q_temp_offset", 0.0))
             tau_max = float(getattr(self.opt_config, "draft_tau_max", 0.0))
@@ -324,6 +327,9 @@ def propose(
         sampling_metadata: SamplingMetadata,
         mm_embeds: Optional[list[torch.Tensor]] = None,
     ) -> torch.Tensor:
+        # Store sampling_metadata so _sample_draft_tokens() can access target temperature
+        self._current_sampling_metadata = sampling_metadata
+
         num_tokens = target_token_ids.shape[0]
         batch_size = next_token_ids.shape[0]