[spec] Fix index_share_for_mtp_iteration being a no-op in EAGLE MTP draft (#29654)

python/sglang/srt/model_executor/forward_batch_info.py

@@ -430,8 +430,8 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
     # For hidden states before normal
     return_hidden_states_before_norm: bool = False
 
-    # For NSA/DSA topk_indices reuse across forward calls (e.g., EAGLE draft)
-    topk_indices: Optional[torch.Tensor] = None
+    # Gate for reusing the first MTP draft step's indexer topk across steps;
+    # the carried topk lives on spec_info (see EagleDraftInput.mtp_topk_indices).
     reuse_mtp_topk_indices: Optional[bool] = False
 
     # === Forward-derived (built in init_new on the forward stream; FB-owned) ===

python/sglang/srt/models/deepseek_nextn.py

@@ -228,13 +228,13 @@ class DeepseekModelNextN(nn.Module):
                     residual,
                     zero_allocator,
                     prev_topk_indices=(
-                        forward_batch.topk_indices
+                        forward_batch.spec_info.mtp_topk_indices
                         if forward_batch.reuse_mtp_topk_indices
                         else None
                     ),
                 )
                 if forward_batch.reuse_mtp_topk_indices:
-                    forward_batch.topk_indices = topk_indices
+                    forward_batch.spec_info.mtp_topk_indices = topk_indices
 
             if not forward_batch.forward_mode.is_idle():
                 if residual is not None:

python/sglang/srt/speculative/eagle_info.py

@@ -159,6 +159,10 @@ class EagleDraftInput(SpecInput):
     hidden_states: Optional[torch.Tensor] = None
     capture_hidden_mode: CaptureHiddenMode = CaptureHiddenMode.FULL
 
+    # Survives across draft steps: spec_info is shared by reference across the
+    # per-step forwards (each runs on a copied ForwardBatch, dropping writebacks).
+    mtp_topk_indices: Optional[torch.Tensor] = None
+
     # Per-req bonus token (the "+1" target prediction at end of each accept
     # chain); the worker copies it here post-extend for next iter's draft.
     bonus_tokens: torch.Tensor = None

python/sglang/srt/speculative/eagle_worker_v2.py

@@ -612,7 +612,7 @@ class EagleDraftWorker(EagleDraftWorkerBase):
         scores = None
         if self.index_share_for_mtp_iteration:
             forward_batch.reuse_mtp_topk_indices = True
-            forward_batch.topk_indices = None
+            spec_info.mtp_topk_indices = None
         for i in range(self.speculative_num_steps):
             input_ids, hidden_states, scores, tree_info = select_top_k_tokens(
                 i, topk_p, topk_index, hidden_states, scores, self.topk
@@ -688,7 +688,7 @@ class EagleDraftWorker(EagleDraftWorkerBase):
             forward_batch.positions.add_(1)
 
         if self.index_share_for_mtp_iteration:
-            forward_batch.topk_indices = None
+            spec_info.mtp_topk_indices = None
             forward_batch.reuse_mtp_topk_indices = False
 
         # Organize the results