[Spec] Unify decode KV-commit bookkeeping across spec-v2 workers (#28754)

python/sglang/srt/managers/scheduler_components/batch_result_processor.py

@@ -558,13 +558,10 @@ class SchedulerBatchResultProcessor:
         for i, req in enumerate(batch.reqs):
             accept_tokens = next_token_ids[i * stride : i * stride + accept_lens[i]]
 
-            if req.is_retracted:
-                # reset_for_retract() already zeroes committed/allocated KV.
+            if req.is_retracted or req.finished():
+                # Nothing to settle: no worker pre-claims the bonus, so
+                # kv_committed_len already holds the committed prefix.
                 pass
-            elif req.finished():
-                if not batch.spec_algorithm.is_dflash():
-                    # EAGLE prepare_for_decode pre-claimed the bonus slot.
-                    req.kv_committed_len -= 1
             else:
                 if req.grammar is not None:
                     # Stop accepting once the grammar terminates, so the
@@ -573,13 +570,9 @@ class SchedulerBatchResultProcessor:
                     # grammar.finished.
                     accept_tokens = self._accept_grammar_tokens(req, accept_tokens)
 
+                # Commit the full accepted run (drafts + bonus).
                 num_accept_tokens = len(accept_tokens)
-                if batch.spec_algorithm.is_dflash():
-                    # DFLASH materialized accepted draft tokens plus the bonus token.
-                    req.kv_committed_len += num_accept_tokens
-                else:
-                    # EAGLE prepare_for_decode pre-claimed the bonus slot.
-                    req.kv_committed_len += num_accept_tokens - 1
+                req.kv_committed_len += num_accept_tokens
                 req.spec_verify_ct += 1
 
                 num_correct_drafts = result.num_correct_drafts_per_req_cpu[i]

python/sglang/srt/session/streaming_session.py

@@ -331,9 +331,15 @@ class StreamingSession(BasePrefixCache):
         finished_len = (
             req.finished_len if req.finished_len is not None else len(req.output_ids)
         )
+        target = len(req.origin_input_ids) + finished_len
         self._trim_overshoot(req, finished_len)
 
         slot.save_from_req(req, is_first=is_first)
+        # Inherit the authoritative finished length on the slot, not the lagging
+        # req clock (under overlap + honest committed the clock lags the in-flight
+        # verify by ~1, which would short-change inheritance). Clamp to allocated
+        # to keep committed <= allocated for prepare_for_decode.
+        slot.kv_committed_len = min(target, slot.kv_allocated_len)
 
         # Update req_nodes to this successfully finished request.
         req.session.finish_req(req)

python/sglang/srt/speculative/eagle_info_v2.py

@@ -47,19 +47,15 @@ class EagleDraftInputV2Mixin:
         num_needed_tokens = 0
         for i, r in enumerate(batch.reqs):
             cur = r.kv_allocated_len
-            # max(cur, ...) clamps so adaptive downswitch (smaller alloc_len_per_decode)
-            # cannot make nxt < cur and corrupt allocator state. kv_committed_len lags
-            # batch.seq_lens by ~1 verify in overlap mode, so we react to adaptive
-            # switches one batch later than a seq_lens-based baseline; the 2*alloc
-            # over-allocation buffer absorbs that lag.
+            # max(cur, ...) clamps so adaptive downswitch cannot make nxt < cur.
+            # kv_committed_len is honest (bonus committed in resolve, not here),
+            # so it lags batch.seq_lens by ~1 verify in overlap; 2*alloc absorbs.
             nxt = max(cur, r.kv_committed_len + double_alloc)
             cur_kv_lens[i] = cur
             nxt_kv_lens[i] = nxt
             num_needed_tokens += nxt - cur
             r.kv_allocated_len = nxt
             r.decode_batch_idx += 1
-            # Pre-claim bonus slot here (like normal decode); resolve subtracts 1.
-            r.kv_committed_len += 1
 
         cur_kv_lens_cpu = torch.tensor(cur_kv_lens, dtype=torch.int32, device="cpu")
         nxt_kv_lens_cpu = torch.tensor(nxt_kv_lens, dtype=torch.int32, device="cpu")