[Spec V1] Split draft-extend phase from EagleDraftInput into new EagleDraftExtendInput (#24859)

python/sglang/srt/model_executor/forward_batch_info.py

@@ -989,17 +989,18 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
             self.extend_seq_lens = self._pad_tensor_to_size(self.extend_seq_lens, bs)
 
         if self.spec_info is not None and self.spec_info.is_draft_input():
-            # FIXME(lsyin): remove this isinstance logic
             spec_info = self.spec_info
             self.output_cache_loc_backup = self.out_cache_loc
             self.hidden_states_backup = spec_info.hidden_states
-            if spec_info.topk_p is not None:
+            # spec_info is EagleDraftInput | EagleDraftExtendInput; each carries
+            # a disjoint subset of the fields below, so getattr-guard each one.
+            if getattr(spec_info, "topk_p", None) is not None:
                 spec_info.topk_p = self._pad_tensor_to_size(spec_info.topk_p, bs)
-            if spec_info.topk_index is not None:
+            if getattr(spec_info, "topk_index", None) is not None:
                 spec_info.topk_index = self._pad_tensor_to_size(
                     spec_info.topk_index, bs
                 )
-            if spec_info.num_accepted_drafts is not None:
+            if getattr(spec_info, "num_accepted_drafts", None) is not None:
                 spec_info.num_accepted_drafts = self._pad_tensor_to_size(
                     spec_info.num_accepted_drafts, bs
                 )

python/sglang/srt/speculative/eagle_draft_extend_cuda_graph_runner.py

@@ -26,7 +26,7 @@ from sglang.srt.model_executor.forward_batch_info import (
     ForwardMode,
 )
 from sglang.srt.model_executor.input_buffers import ForwardInputBuffers
-from sglang.srt.speculative.eagle_info import EagleDraftInput
+from sglang.srt.speculative.eagle_info import EagleDraftExtendInput
 from sglang.srt.speculative.spec_utils import fast_topk
 from sglang.srt.utils import (
     require_attn_tp_gather,
@@ -360,7 +360,7 @@ class EAGLEDraftExtendCudaGraphRunner:
         else:
             global_dp_buffer_len = None
 
-        spec_info = EagleDraftInput(
+        spec_info = EagleDraftExtendInput(
             hidden_states=hidden_states,
             num_accepted_drafts=num_accepted_drafts,
             num_accepted_tokens=num_accepted_tokens,

python/sglang/srt/speculative/eagle_info.py

@@ -240,15 +240,14 @@ class EagleVerifyInput(SpecInput, EagleVerifyInputV2Mixin):
         accepted token logits.
         """
         if batch.forward_mode.is_idle():
-            next_draft_input = EagleDraftInput.create_idle_input(
+            draft_extend_input = EagleDraftExtendInput.create_idle_input(
                 device=batch.device,
                 hidden_size=batch.model_config.spec_hidden_size,
                 dtype=batch.model_config.dtype,
-                topk=self.topk,
                 capture_hidden_mode=CaptureHiddenMode.LAST,
             )
             return EagleVerifyOutput.create_idle(
-                next_draft_input=next_draft_input,
+                draft_extend_input=draft_extend_input,
                 logits_output=logits_output,
                 device=batch.device,
                 spec_steps=self.spec_steps,
@@ -545,21 +544,21 @@ class EagleVerifyInput(SpecInput, EagleVerifyInputV2Mixin):
             batch.seq_lens.add_(num_accepted_drafts + 1)
             batch.seq_lens_cpu.add_(num_accepted_tokens_cpu)
 
-            next_draft_input = EagleDraftInput(
+            draft_extend_input = EagleDraftExtendInput(
                 hidden_states=batch.spec_info.hidden_states[accept_index],
                 num_accepted_drafts=num_accepted_drafts,
                 num_accepted_tokens=num_accepted_drafts + 1,
                 num_accepted_tokens_cpu=num_accepted_tokens_list,
+                input_ids=accept_tokens,
+                seq_lens=batch.seq_lens,
+                seq_lens_cpu=batch.seq_lens_cpu,
+                req_pool_indices=batch.req_pool_indices,
             )
 
             return EagleVerifyOutput(
-                next_draft_input=next_draft_input,
+                draft_extend_input=draft_extend_input,
                 logits_output=logits_output,
                 accept_tokens=accept_tokens,
-                unfinished_accept_tokens=accept_tokens,
-                seq_lens_for_draft_extend=batch.seq_lens,
-                seq_lens_for_draft_extend_cpu=batch.seq_lens_cpu,
-                req_pool_indices_for_draft_extend=batch.req_pool_indices,
                 num_accepted_drafts_per_req_cpu=num_accepted_drafts_list,
                 accepted_indices=accept_index,
             )
@@ -614,51 +613,30 @@ class EagleVerifyInput(SpecInput, EagleVerifyInputV2Mixin):
                 unfinished_num_accepted_drafts = num_accepted_drafts[
                     unfinished_index_device
                 ]
-                unfinished_accept_tokens = predict[unfinished_accept_index]
-                seq_lens_for_draft_extend = batch.seq_lens[unfinished_index_device]
-                seq_lens_for_draft_extend_cpu = batch.seq_lens_cpu[unfinished_index]
-                req_pool_indices_for_draft_extend = batch.req_pool_indices[
-                    unfinished_index_device
-                ]
-                next_draft_input = EagleDraftInput(
+                draft_extend_input = EagleDraftExtendInput(
                     hidden_states=batch.spec_info.hidden_states[
                         unfinished_accept_index
                     ],
                     num_accepted_tokens_cpu=draft_input_num_accepted_tokens_cpu,
                     num_accepted_drafts=unfinished_num_accepted_drafts,
                     num_accepted_tokens=unfinished_num_accepted_drafts + 1,
+                    input_ids=predict[unfinished_accept_index],
+                    seq_lens=batch.seq_lens[unfinished_index_device],
+                    seq_lens_cpu=batch.seq_lens_cpu[unfinished_index],
+                    req_pool_indices=batch.req_pool_indices[unfinished_index_device],
                 )
             else:
-                unfinished_accept_tokens = torch.empty(
-                    (0,), dtype=accept_tokens.dtype, device=accept_tokens.device
-                )
-                seq_lens_for_draft_extend = torch.empty(
-                    (0,), dtype=batch.seq_lens.dtype, device=batch.seq_lens.device
-                )
-                seq_lens_for_draft_extend_cpu = torch.empty(
-                    (0,), dtype=batch.seq_lens_cpu.dtype
-                )
-                req_pool_indices_for_draft_extend = torch.empty(
-                    (0,),
-                    dtype=batch.req_pool_indices.dtype,
-                    device=batch.req_pool_indices.device,
-                )
-                next_draft_input = EagleDraftInput.create_idle_input(
+                draft_extend_input = EagleDraftExtendInput.create_idle_input(
                     device=batch.device,
                     hidden_size=batch.model_config.spec_hidden_size,
                     dtype=batch.model_config.dtype,
-                    topk=self.topk,
                     capture_hidden_mode=CaptureHiddenMode.LAST,
                 )
 
             return EagleVerifyOutput(
-                next_draft_input=next_draft_input,
+                draft_extend_input=draft_extend_input,
                 logits_output=logits_output,
                 accept_tokens=accept_tokens,
-                unfinished_accept_tokens=unfinished_accept_tokens,
-                seq_lens_for_draft_extend=seq_lens_for_draft_extend,
-                seq_lens_for_draft_extend_cpu=seq_lens_for_draft_extend_cpu,
-                req_pool_indices_for_draft_extend=req_pool_indices_for_draft_extend,
                 num_accepted_drafts_per_req_cpu=num_accepted_drafts_list,
                 accepted_indices=accept_index,
             )
@@ -666,42 +644,33 @@ class EagleVerifyInput(SpecInput, EagleVerifyInputV2Mixin):
 
 @dataclass
 class EagleDraftInput(SpecInput, EagleDraftInputV2Mixin):
-    # The inputs for decode
     # shape: (b, topk)
     topk_p: torch.Tensor = None
     topk_index: torch.Tensor = None
-    # shape: (b, hidden_size) when consumed by `draft` forward (one hidden per req);
-    # shape: (total_accepted, hidden_size) when consumed by `draft_extend` forward
-    # (one hidden per accepted token). Workers maintain this invariant locally;
-    # there is no type-level guard. Don't add new readers without checking phase.
+    # shape: (b, hidden_size) - one hidden per req, consumed by `draft` forward.
     hidden_states: torch.Tensor = None
     capture_hidden_mode: CaptureHiddenMode = CaptureHiddenMode.FULL
 
-    # Inputs for extend
-    # shape: (b,)
-    # `num_accepted_drafts` and `num_accepted_tokens` are kept in sync:
-    # `num_accepted_tokens = num_accepted_drafts + 1` (per-req, one bonus per req).
-    # Storing both avoids repeated `+ 1` at every consumer (attn backends, kernels).
+    # Per-req bonus token (the "+1" target prediction at end of each accept
+    # chain). Written by `EagleDraftExtendInput.prepare_extend_after_decode`;
+    # the worker copies it here for next iter's draft.
     bonus_tokens: torch.Tensor = None
-    num_accepted_drafts: torch.Tensor = None
-    num_accepted_tokens: torch.Tensor = None
-    # Read by attention backends during draft-extend forward; kept on the
-    # dataclass because the backends access it via `forward_batch.spec_info`.
-    num_accepted_tokens_cpu: List[int] = None
 
-    # Inputs for the attention backends
     # shape: (b + 1,)
     kv_indptr: torch.Tensor = None
     kv_indices: torch.Tensor = None
 
-    # Shape info for padding
     num_tokens_per_req: int = -1
     num_tokens_for_logprob_per_req: int = -1
 
-    # Inputs for V2 overlap worker
+    # V2 overlap worker only
     future_indices: Optional[FutureIndices] = None
     new_seq_lens: Optional[torch.Tensor] = None
     verify_done: Optional[torch.cuda.Event] = None
+    # V2 reuses `EagleDraftInput` across phases (V1 has a separate
+    # `EagleDraftExtendInput` for these). Set during V2's draft-extend.
+    num_accepted_drafts: Optional[torch.Tensor] = None
+    num_accepted_tokens: Optional[torch.Tensor] = None
 
     def __post_init__(self):
         super().__init__(SpecInputType.EAGLE_DRAFT)
@@ -741,81 +710,8 @@ class EagleDraftInput(SpecInput, EagleDraftInputV2Mixin):
             topk_index=torch.empty((0, topk), device=device, dtype=torch.int64),
             capture_hidden_mode=capture_hidden_mode,
             new_seq_lens=torch.empty((0,), device=device, dtype=torch.int32),
-            num_accepted_drafts=torch.empty((0,), device=device, dtype=torch.int32),
-            num_accepted_tokens=torch.empty((0,), device=device, dtype=torch.int32),
-            num_accepted_tokens_cpu=[],
         )
 
-    def prepare_extend_after_decode(
-        self,
-        batch: ScheduleBatch,
-        verify_output: "EagleVerifyOutput",
-        speculative_num_steps: int,
-    ):
-
-        if batch.forward_mode.is_idle():
-            return
-
-        # All transient verify->extend handoff state is read off `verify_output`,
-        # not from `self`. The kernel below populates `self.bonus_tokens`
-        # ([bs] per-req) for the next decode round; that is the only state on
-        # `self` that survives past this method.
-        batch.input_ids = verify_output.unfinished_accept_tokens
-        batch.extend_lens = batch.spec_info.num_accepted_tokens_cpu
-        batch.extend_num_tokens = sum(batch.extend_lens)
-        batch.seq_lens = verify_output.seq_lens_for_draft_extend
-        batch.seq_lens_cpu = verify_output.seq_lens_for_draft_extend_cpu
-        batch.req_pool_indices = verify_output.req_pool_indices_for_draft_extend
-        batch.return_logprob = False
-        batch.return_hidden_states = False
-
-        self.capture_hidden_mode = CaptureHiddenMode.LAST
-        self.positions = torch.empty_like(batch.input_ids, dtype=torch.long)
-        self.bonus_tokens = torch.empty_like(
-            self.num_accepted_tokens, dtype=torch.int32
-        )
-
-        create_extend_after_decode_spec_info[(len(batch.seq_lens),)](
-            batch.input_ids,
-            batch.seq_lens,
-            self.num_accepted_tokens,
-            self.positions,
-            self.bonus_tokens,
-            next_power_of_2(max(speculative_num_steps + 1, len(batch.seq_lens))),
-        )
-
-    def generate_attn_arg_prefill(
-        self,
-        req_pool_indices: torch.Tensor,
-        paged_kernel_lens: torch.Tensor,
-        paged_kernel_lens_sum: int,
-        req_to_token: torch.Tensor,
-    ):
-        device = req_pool_indices.device
-        bs = self.num_accepted_drafts.numel()
-        qo_indptr = torch.zeros((bs + 1,), dtype=torch.int32, device=device)
-        qo_indptr[1:] = torch.cumsum(self.num_accepted_tokens, dim=0)
-        cum_kv_seq_len = torch.zeros((bs + 1,), dtype=torch.int32, device=device)
-        cum_kv_seq_len[1:] = torch.cumsum(paged_kernel_lens, dim=0)
-
-        if paged_kernel_lens_sum is None:
-            paged_kernel_lens_sum = cum_kv_seq_len[-1]
-
-        kv_indices = torch.empty(
-            paged_kernel_lens_sum, dtype=torch.int32, device=device
-        )
-
-        create_flashinfer_kv_indices_triton[(bs,)](
-            req_to_token,
-            req_pool_indices,
-            paged_kernel_lens,
-            cum_kv_seq_len,
-            None,
-            kv_indices,
-            req_to_token.size(1),
-        )
-        return kv_indices, cum_kv_seq_len, qo_indptr, None
-
     def filter_batch(self, new_indices: torch.Tensor, has_been_filtered: bool = True):
         if self.future_indices is not None:
             self.future_indices.indices = self.future_indices.indices[new_indices]
@@ -871,29 +767,155 @@ class EagleDraftInput(SpecInput, EagleDraftInputV2Mixin):
         self.topk_index = torch.cat([self.topk_index, spec_info.topk_index])
 
 
+@dataclass
+class EagleDraftExtendInput(SpecInput):
+    """Inputs to the draft-extend forward (the per-accepted-token pass after verify).
+
+    Produced by `EagleVerifyInput.verify`, installed on `batch.spec_info` for
+    the draft-extend forward, then replaced with a fresh `EagleDraftInput` for
+    the next iter's draft.
+    """
+
+    # shape: (total_accepted, hidden_size). Sliced from verify-time hidden_states
+    # by accept_index; consumed by the draft-extend forward.
+    hidden_states: torch.Tensor = None
+
+    # Per-req accept counts. `num_accepted_tokens = num_accepted_drafts + 1`.
+    # Both kept for cuda-graph buffer indexing and the
+    # `create_extend_after_decode_spec_info` kernel.
+    num_accepted_drafts: torch.Tensor = None
+    num_accepted_tokens: torch.Tensor = None
+    # CPU view, read by attention backends during the extend forward.
+    num_accepted_tokens_cpu: List[int] = None
+
+    # Batch-state slices for the draft-extend forward. Set by verify (sliced to
+    # reqs continuing into next iter). `prepare_extend_after_decode` copies
+    # these onto `batch.{input_ids, seq_lens, seq_lens_cpu, req_pool_indices}`.
+    #   - input_ids:        accept tokens flat over surviving reqs
+    #   - seq_lens / _cpu:  per-req sequence length (post-accept)
+    #   - req_pool_indices: per-req kv-pool slot
+    input_ids: torch.Tensor = None
+    seq_lens: torch.Tensor = None
+    seq_lens_cpu: torch.Tensor = None
+    req_pool_indices: torch.Tensor = None
+
+    # Set by `prepare_extend_after_decode`:
+    #   - positions: kernel-written, shape `[total_accepted]`.
+    #   - bonus_tokens: kernel-written, shape `[bs]`. The worker reads this
+    #     post-extend to populate next iter's `EagleDraftInput.bonus_tokens`.
+    positions: Optional[torch.Tensor] = None
+    bonus_tokens: Optional[torch.Tensor] = None
+
+    capture_hidden_mode: CaptureHiddenMode = CaptureHiddenMode.LAST
+    num_tokens_per_req: int = -1
+    num_tokens_for_logprob_per_req: int = 1
+
+    def __post_init__(self):
+        super().__init__(SpecInputType.EAGLE_DRAFT_EXTEND)
+
+    def get_spec_adjust_token_coefficient(self) -> Tuple[int, int]:
+        return self.num_tokens_per_req, self.num_tokens_for_logprob_per_req
+
+    @classmethod
+    def create_idle_input(
+        cls,
+        device: torch.device,
+        hidden_size: int,
+        dtype: torch.dtype,
+        capture_hidden_mode: CaptureHiddenMode = CaptureHiddenMode.LAST,
+    ) -> "EagleDraftExtendInput":
+        return cls(
+            hidden_states=torch.empty((0, hidden_size), device=device, dtype=dtype),
+            num_accepted_drafts=torch.empty((0,), device=device, dtype=torch.int32),
+            num_accepted_tokens=torch.empty((0,), device=device, dtype=torch.int32),
+            num_accepted_tokens_cpu=[],
+            input_ids=torch.empty((0,), device=device, dtype=torch.long),
+            seq_lens=torch.empty((0,), device=device, dtype=torch.int32),
+            seq_lens_cpu=torch.empty((0,), dtype=torch.int32),
+            req_pool_indices=torch.empty((0,), device=device, dtype=torch.int64),
+            capture_hidden_mode=capture_hidden_mode,
+        )
+
+    def prepare_extend_after_decode(
+        self,
+        batch: ScheduleBatch,
+        speculative_num_steps: int,
+    ):
+        # Caller must have installed `self` as `batch.spec_info` before calling.
+        assert batch.spec_info is self
+        if batch.forward_mode.is_idle():
+            return
+
+        # The kernel below populates `self.positions` and `self.bonus_tokens`;
+        # the worker reads `self.bonus_tokens` to construct next iter's
+        # `EagleDraftInput`.
+        batch.input_ids = self.input_ids
+        batch.extend_lens = self.num_accepted_tokens_cpu
+        batch.extend_num_tokens = sum(batch.extend_lens)
+        batch.seq_lens = self.seq_lens
+        batch.seq_lens_cpu = self.seq_lens_cpu
+        batch.req_pool_indices = self.req_pool_indices
+        batch.return_logprob = False
+        batch.return_hidden_states = False
+
+        self.capture_hidden_mode = CaptureHiddenMode.LAST
+        self.positions = torch.empty_like(batch.input_ids, dtype=torch.long)
+        self.bonus_tokens = torch.empty_like(
+            self.num_accepted_tokens, dtype=torch.int32
+        )
+
+        create_extend_after_decode_spec_info[(len(batch.seq_lens),)](
+            batch.input_ids,
+            batch.seq_lens,
+            self.num_accepted_tokens,
+            self.positions,
+            self.bonus_tokens,
+            next_power_of_2(max(speculative_num_steps + 1, len(batch.seq_lens))),
+        )
+
+    def generate_attn_arg_prefill(
+        self,
+        req_pool_indices: torch.Tensor,
+        paged_kernel_lens: torch.Tensor,
+        paged_kernel_lens_sum: Optional[int],
+        req_to_token: torch.Tensor,
+    ):
+        device = req_pool_indices.device
+        bs = self.num_accepted_drafts.numel()
+        qo_indptr = torch.zeros((bs + 1,), dtype=torch.int32, device=device)
+        qo_indptr[1:] = torch.cumsum(self.num_accepted_tokens, dim=0)
+        cum_kv_seq_len = torch.zeros((bs + 1,), dtype=torch.int32, device=device)
+        cum_kv_seq_len[1:] = torch.cumsum(paged_kernel_lens, dim=0)
+
+        if paged_kernel_lens_sum is None:
+            paged_kernel_lens_sum = cum_kv_seq_len[-1]
+
+        kv_indices = torch.empty(
+            paged_kernel_lens_sum, dtype=torch.int32, device=device
+        )
+
+        create_flashinfer_kv_indices_triton[(bs,)](
+            req_to_token,
+            req_pool_indices,
+            paged_kernel_lens,
+            cum_kv_seq_len,
+            None,
+            kv_indices,
+            req_to_token.size(1),
+        )
+        return kv_indices, cum_kv_seq_len, qo_indptr, None
+
+
 @dataclass
 class EagleVerifyOutput:
-    # Next iter's persistent draft state, ready to be installed as `batch.spec_info`.
-    next_draft_input: EagleDraftInput
+    # Next iter's draft-extend input, installed as `batch.spec_info` for the
+    # draft-extend forward.
+    draft_extend_input: EagleDraftExtendInput
     # Logit outputs from target worker.
     logits_output: LogitsProcessorOutput
     # All accepted tokens flat across all reqs incl. those that finished this
     # step. Includes the bonus token. Used for output processing.
     accept_tokens: torch.Tensor
-    # Below are transient handoff fields for the next iter's draft-extend pass.
-    # They are scoped to the verify -> prepare_extend_after_decode window only;
-    # `prepare_extend_after_decode` reads them off this object via method arg
-    # rather than smuggling them through `EagleDraftInput`.
-    #
-    # Subset of `accept_tokens` for reqs continuing into next iter's draft-extend
-    # forward (= `accept_tokens` when no req finished; flat over unfinished
-    # reqs only otherwise). Becomes `batch.input_ids` for that forward pass.
-    unfinished_accept_tokens: torch.Tensor
-    # `batch.seq_lens` / `batch.seq_lens_cpu` / `batch.req_pool_indices` to
-    # use for the next iter's draft-extend forward; sliced to surviving reqs.
-    seq_lens_for_draft_extend: torch.Tensor
-    seq_lens_for_draft_extend_cpu: torch.Tensor
-    req_pool_indices_for_draft_extend: torch.Tensor
     # Accepted token length per sequence in a batch in CPU (full set).
     num_accepted_drafts_per_req_cpu: List[int]
     # Accepted indices from logits_output.next_token_logits
@@ -903,21 +925,15 @@ class EagleVerifyOutput:
     def create_idle(
         cls,
         *,
-        next_draft_input: EagleDraftInput,
+        draft_extend_input: EagleDraftExtendInput,
         logits_output: LogitsProcessorOutput,
         device: torch.device,
         spec_steps: int,
     ) -> "EagleVerifyOutput":
         return cls(
-            next_draft_input=next_draft_input,
+            draft_extend_input=draft_extend_input,
             logits_output=logits_output,
             accept_tokens=torch.empty(0, dtype=torch.long, device=device),
-            unfinished_accept_tokens=torch.empty(0, dtype=torch.long, device=device),
-            seq_lens_for_draft_extend=torch.empty(0, dtype=torch.int32, device=device),
-            seq_lens_for_draft_extend_cpu=torch.empty(0, dtype=torch.int32),
-            req_pool_indices_for_draft_extend=torch.empty(
-                0, dtype=torch.int64, device=device
-            ),
             num_accepted_drafts_per_req_cpu=[],
             accepted_indices=torch.full(
                 (0, spec_steps + 1), -1, dtype=torch.int32, device=device

python/sglang/srt/speculative/eagle_worker.py

@@ -46,6 +46,7 @@ from sglang.srt.speculative.eagle_draft_extend_cuda_graph_runner import (
     EAGLEDraftExtendCudaGraphRunner,
 )
 from sglang.srt.speculative.eagle_info import (
+    EagleDraftExtendInput,
     EagleDraftInput,
     EagleVerifyInput,
     EagleVerifyOutput,
@@ -480,14 +481,13 @@ class EAGLEWorker(TpModelWorker):
             with self.draft_tp_context(
                 self.draft_model_runner.tp_group
             ), speculative_moe_backend_context(), speculative_moe_a2a_backend_context():
-                spec_info = self.draft(batch)
+                verify_input = self.draft(batch)
 
             set_time_batch(batch.reqs, "set_spec_draft_end_time", trace_only=True)
             set_time_batch(batch.reqs, "set_spec_verify_start_time", trace_only=True)
 
-            logits_output, verify_output, can_run_cuda_graph = self.verify(
-                batch, spec_info
-            )
+            batch.spec_info = verify_input
+            logits_output, verify_output, can_run_cuda_graph = self.verify(batch)
 
             if get_global_tracing_enabled():
                 for idx, req in enumerate(batch.reqs):
@@ -503,12 +503,24 @@ class EAGLEWorker(TpModelWorker):
             ), speculative_moe_backend_context(), speculative_moe_a2a_backend_context():
                 # NOTE: We should use `check_forward_draft_extend_after_decode`
                 # when DP attention is enabled, but it is slow. Skip it for now.
+                draft_extend_input = verify_output.draft_extend_input
                 if (
                     self.server_args.enable_dp_attention
-                    or verify_output.unfinished_accept_tokens.shape[0] > 0
+                    or draft_extend_input.input_ids.shape[0] > 0
                 ):
-                    # decode is not finished
-                    self.forward_draft_extend_after_decode(batch, verify_output)
+                    # decode is not finished; stash for extend, then restash
+                    # the next-iter EagleDraftInput it returns.
+                    batch.spec_info = draft_extend_input
+                    next_draft_input = self.forward_draft_extend_after_decode(batch)
+                    batch.spec_info = next_draft_input
+                else:
+                    # All reqs finished and dp_attention isn't forcing extend.
+                    # Stash an empty EagleDraftInput so next iter's merge_batch
+                    # short-circuits on None hidden_states (EagleVerifyInput
+                    # has no merge_batch).
+                    batch.spec_info = EagleDraftInput(
+                        capture_hidden_mode=CaptureHiddenMode.LAST,
+                    )
 
             set_time_batch(
                 batch.reqs, "set_spec_draft_extend_end_time", trace_only=True
@@ -528,7 +540,7 @@ class EAGLEWorker(TpModelWorker):
             )
 
     def check_forward_draft_extend_after_decode(self, verify_output: EagleVerifyOutput):
-        local_need_forward = verify_output.unfinished_accept_tokens.shape[0] > 0
+        local_need_forward = verify_output.draft_extend_input.input_ids.shape[0] > 0
         if not self.server_args.enable_dp_attention:
             return local_need_forward
 
@@ -890,7 +902,8 @@ class EAGLEWorker(TpModelWorker):
         # allocator and kv cache pool are shared with target worker
         pass
 
-    def verify(self, batch: ScheduleBatch, spec_info: EagleVerifyInput):
+    def verify(self, batch: ScheduleBatch):
+        spec_info: EagleVerifyInput = batch.spec_info
         seq_lens_pre_verify = batch.seq_lens.clone()
         spec_info.prepare_for_verify(batch, self.page_size)
         spec_info.num_tokens_per_req = self.speculative_num_steps + 1
@@ -900,7 +913,6 @@ class EAGLEWorker(TpModelWorker):
             if not batch.forward_mode.is_idle()
             else ForwardMode.IDLE
         )
-        batch.spec_info = spec_info
 
         model_worker_batch = batch.get_model_worker_batch(
             seq_lens_cpu_cache=spec_info.seq_lens_cpu
@@ -977,7 +989,6 @@ class EAGLEWorker(TpModelWorker):
         batch.forward_mode = (
             ForwardMode.DECODE if not batch.forward_mode.is_idle() else ForwardMode.IDLE
         )
-        batch.spec_info = res.next_draft_input
 
         return logits_output, res, can_run_cuda_graph
 
@@ -1105,20 +1116,20 @@ class EAGLEWorker(TpModelWorker):
         self.capture_for_decode(logits_output, forward_batch.spec_info)
 
     def forward_draft_extend_after_decode(
-        self, batch: ScheduleBatch, verify_output: EagleVerifyOutput
-    ):
-        assert isinstance(batch.spec_info, EagleDraftInput)
+        self, batch: ScheduleBatch
+    ) -> EagleDraftInput:
+        draft_extend_input: EagleDraftExtendInput = batch.spec_info
+
         # Backup fields that will be modified in-place
         seq_lens_backup = batch.seq_lens.clone()
         seq_lens_cpu_backup = batch.seq_lens_cpu.clone()
         req_pool_indices_backup = batch.req_pool_indices
-        num_accepted_drafts_backup = batch.spec_info.num_accepted_drafts.clone()
-        num_accepted_tokens_backup = batch.spec_info.num_accepted_tokens.clone()
         return_logprob_backup = batch.return_logprob
 
         input_is_idle = batch.forward_mode.is_idle()
 
-        if not input_is_idle and verify_output.unfinished_accept_tokens.numel() == 0:
+        if not input_is_idle and draft_extend_input.input_ids.numel() == 0:
+            # All reqs finished this verify; swap to an idle ExtendInput.
             batch = batch.copy()
             batch.prepare_for_idle()
             hidden_size = (
@@ -1127,19 +1138,19 @@ class EAGLEWorker(TpModelWorker):
                 and self.eagle_use_aux_hidden_state
                 else self.model_config.spec_hidden_size
             )
-            batch.spec_info = EagleDraftInput.create_idle_input(
+            draft_extend_input = EagleDraftExtendInput.create_idle_input(
                 device=self.device,
                 hidden_size=hidden_size,
                 dtype=self.model_config.dtype,
-                topk=self.topk,
                 capture_hidden_mode=CaptureHiddenMode.LAST,
             )
+            batch.spec_info = draft_extend_input
 
-        batch.spec_info.num_tokens_per_req = self.speculative_num_steps + 1
-        batch.spec_info.num_tokens_for_logprob_per_req = 1
-        batch.spec_info.prepare_extend_after_decode(
+        # Phase 1: prepare extend (kernel writes draft_extend_input.{positions, bonus_tokens})
+        draft_extend_input.num_tokens_per_req = self.speculative_num_steps + 1
+        draft_extend_input.num_tokens_for_logprob_per_req = 1
+        draft_extend_input.prepare_extend_after_decode(
             batch,
-            verify_output=verify_output,
             speculative_num_steps=self.speculative_num_steps,
         )
         batch.forward_mode = (
@@ -1159,7 +1170,7 @@ class EAGLEWorker(TpModelWorker):
         else:
             forward_batch.seq_lens_sum = batch.seq_lens.sum().item()
 
-        # Run
+        # Phase 2: run draft-extend forward
         can_cuda_graph = (
             self.cuda_graph_runner_for_draft_extend
             and self.cuda_graph_runner_for_draft_extend.can_run(forward_batch)
@@ -1168,11 +1179,10 @@ class EAGLEWorker(TpModelWorker):
             logits_output = self.cuda_graph_runner_for_draft_extend.replay(
                 forward_batch
             )
-            forward_batch.spec_info.topk_p, forward_batch.spec_info.topk_index = (
-                logits_output.topk_p,
-                logits_output.topk_index,
-            )
-            forward_batch.spec_info.hidden_states = logits_output.hidden_states
+            # cuda-graph replay populates logits_output.{topk_p, topk_index, hidden_states}.
+            topk_p = logits_output.topk_p
+            topk_index = logits_output.topk_index
+            hidden_states = logits_output.hidden_states
         else:
             forward_batch.can_run_dp_cuda_graph = False
             if not forward_batch.forward_mode.is_idle():
@@ -1185,24 +1195,36 @@ class EAGLEWorker(TpModelWorker):
             logits_output = self.draft_model_runner.forward(
                 forward_batch, skip_attn_backend_init=True
             ).logits_output
-            self.capture_for_decode(logits_output, forward_batch.spec_info)
+            # Non-cuda-graph path: compute topk_p / topk_index inline.
+            probs = torch.softmax(logits_output.next_token_logits, dim=-1)
+            topk_p, topk_index = fast_topk(probs, self.topk, dim=-1)
+            hidden_states = logits_output.hidden_states
 
         maybe_detect_nan(
             logits_output.next_token_logits,
             f"draft_extend_after_decode (cuda_graph={can_cuda_graph})",
         )
 
-        # Restore backup.
-        # This is because `seq_lens` can be modified in `prepare_extend_after_decode`
+        # Phase 3: assemble next-iter EagleDraftInput from extend output
+        next_draft_input = EagleDraftInput(
+            bonus_tokens=draft_extend_input.bonus_tokens,
+            hidden_states=hidden_states,
+            topk_p=topk_p,
+            topk_index=topk_index,
+            capture_hidden_mode=CaptureHiddenMode.FULL,
+        )
+
+        # Restore batch fields. `seq_lens` etc. were modified by
+        # `prepare_extend_after_decode`. Caller installs `next_draft_input` as
+        # `batch.spec_info`.
         batch.forward_mode = (
             ForwardMode.DECODE if not input_is_idle else ForwardMode.IDLE
         )
         batch.seq_lens = seq_lens_backup
         batch.seq_lens_cpu = seq_lens_cpu_backup
         batch.req_pool_indices = req_pool_indices_backup
-        batch.spec_info.num_accepted_drafts = num_accepted_drafts_backup
-        batch.spec_info.num_accepted_tokens = num_accepted_tokens_backup
         batch.return_logprob = return_logprob_backup
+        return next_draft_input
 
     def capture_for_decode(
         self, logits_output: LogitsProcessorOutput, draft_input: EagleDraftInput

python/sglang/srt/speculative/frozen_kv_mtp_info.py

@@ -18,6 +18,7 @@ from typing import Dict
 
 from sglang.srt.mem_cache.memory_pool import KVCache
 from sglang.srt.speculative.eagle_info import (
+    EagleDraftExtendInput,
     EagleDraftInput,
     EagleVerifyInput,
     EagleVerifyOutput,
@@ -53,6 +54,14 @@ class FrozenKVMTPDraftInput(EagleDraftInput):
         SpecInput.__init__(self, SpecInputType.FROZEN_KV_MTP_DRAFT)
 
 
+@dataclass
+class FrozenKVMTPDraftExtendInput(EagleDraftExtendInput):
+    """Draft-extend input for Frozen-KV MTP. Tag-only subclass."""
+
+    def __post_init__(self):
+        SpecInput.__init__(self, SpecInputType.FROZEN_KV_MTP_DRAFT_EXTEND)
+
+
 @dataclass
 class FrozenKVMTPVerifyInput(EagleVerifyInput):
     """Verify input for Frozen-KV MTP."""
@@ -62,21 +71,23 @@ class FrozenKVMTPVerifyInput(EagleVerifyInput):
 
     def verify(self, *args, **kwargs) -> EagleVerifyOutput:
         output = super().verify(*args, **kwargs)
-        output.next_draft_input = _to_frozen_kv_mtp_draft_input(output.next_draft_input)
+        output.draft_extend_input = _to_frozen_kv_mtp_draft_extend_input(
+            output.draft_extend_input
+        )
         return output
 
 
 FrozenKVMTPVerifyOutput = EagleVerifyOutput
 
 
-def _to_frozen_kv_mtp_draft_input(
-    draft_input: EagleDraftInput,
-) -> FrozenKVMTPDraftInput:
-    if isinstance(draft_input, FrozenKVMTPDraftInput):
-        return draft_input
-    return FrozenKVMTPDraftInput(
+def _to_frozen_kv_mtp_draft_extend_input(
+    draft_extend_input: EagleDraftExtendInput,
+) -> FrozenKVMTPDraftExtendInput:
+    if isinstance(draft_extend_input, FrozenKVMTPDraftExtendInput):
+        return draft_extend_input
+    return FrozenKVMTPDraftExtendInput(
         **{
-            field.name: getattr(draft_input, field.name)
-            for field in fields(EagleDraftInput)
+            field.name: getattr(draft_extend_input, field.name)
+            for field in fields(EagleDraftExtendInput)
         }
     )

python/sglang/srt/speculative/frozen_kv_mtp_utils.py

@@ -23,6 +23,7 @@ from sglang.srt.managers.schedule_batch import ScheduleBatch
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.speculative.frozen_kv_mtp_info import (
     FrozenKVMTPContext,
+    FrozenKVMTPDraftExtendInput,
     FrozenKVMTPDraftInput,
 )
 from sglang.srt.speculative.spec_utils import fast_topk
@@ -134,11 +135,8 @@ def select_last_extend_hidden(
 
 
 def select_last_verified_seed(
-    draft_input: FrozenKVMTPDraftInput,
+    draft_input: FrozenKVMTPDraftExtendInput,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
-    if draft_input.num_accepted_tokens is None:
-        return draft_input.bonus_tokens, draft_input.hidden_states
-
     counts = draft_input.num_accepted_tokens.to(torch.long)
     last_indices = torch.cumsum(counts, dim=0) - 1
     return (

python/sglang/srt/speculative/frozen_kv_mtp_worker.py

@@ -43,13 +43,13 @@ from sglang.srt.model_executor.pool_configurator import MemoryPoolConfig
 from sglang.srt.observability.req_time_stats import set_time_batch
 from sglang.srt.observability.trace import get_global_tracing_enabled
 from sglang.srt.server_args import ServerArgs
-from sglang.srt.speculative.eagle_info import EagleVerifyOutput
 from sglang.srt.speculative.eagle_utils import (
     build_tree_kernel_efficient,
     organize_draft_results,
 )
 from sglang.srt.speculative.frozen_kv_mtp_info import (
     FrozenKVMTPContext,
+    FrozenKVMTPDraftExtendInput,
     FrozenKVMTPDraftInput,
     FrozenKVMTPVerifyInput,
     FrozenKVMTPVerifyOutput,
@@ -335,7 +335,7 @@ class FrozenKVMTPWorker(TpModelWorker):
         return select_last_extend_hidden(batch, hidden_states)
 
     def _select_last_verified_seed(
-        self, draft_input: FrozenKVMTPDraftInput
+        self, draft_input: FrozenKVMTPDraftExtendInput
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         return select_last_verified_seed(draft_input)
 
@@ -443,11 +443,12 @@ class FrozenKVMTPWorker(TpModelWorker):
         with self.draft_tp_context(
             self.draft_model_runner.tp_group
         ), speculative_moe_backend_context(), speculative_moe_a2a_backend_context():
-            spec_info = self.draft(batch)
+            verify_input = self.draft(batch)
         set_time_batch(batch.reqs, "set_spec_draft_end_time", trace_only=True)
         set_time_batch(batch.reqs, "set_spec_verify_start_time", trace_only=True)
 
-        logits_output, verify_output, can_run_cuda_graph = self.verify(batch, spec_info)
+        batch.spec_info = verify_input
+        logits_output, verify_output, can_run_cuda_graph = self.verify(batch)
 
         if get_global_tracing_enabled():
             for idx, req in enumerate(batch.reqs):
@@ -458,11 +459,15 @@ class FrozenKVMTPWorker(TpModelWorker):
         with self.draft_tp_context(
             self.draft_model_runner.tp_group
         ), speculative_moe_backend_context(), speculative_moe_a2a_backend_context():
+            draft_extend_input = verify_output.draft_extend_input
             if (
                 self.server_args.enable_dp_attention
-                or batch.spec_info.bonus_tokens.numel()
+                or draft_extend_input.input_ids.numel() > 0
             ):
-                self.forward_draft_extend_after_decode(batch, verify_output)
+                # Stash for the seed step; _run_assistant_seed_step swaps in
+                # a fresh FrozenKVMTPDraftInput for next iter.
+                batch.spec_info = draft_extend_input
+                self.forward_draft_extend_after_decode(batch)
         set_time_batch(batch.reqs, "set_spec_draft_extend_end_time", trace_only=True)
 
         return GenerationBatchResult(
@@ -503,12 +508,13 @@ class FrozenKVMTPWorker(TpModelWorker):
             mm_input_embeds=mm_input_embeds,
         )
 
-    def forward_draft_extend_after_decode(
-        self, batch: ScheduleBatch, verify_output: EagleVerifyOutput
-    ) -> None:
-        assert isinstance(batch.spec_info, FrozenKVMTPDraftInput)
+    def forward_draft_extend_after_decode(self, batch: ScheduleBatch) -> None:
+        draft_extend_input: FrozenKVMTPDraftExtendInput = batch.spec_info
         input_is_idle = batch.forward_mode.is_idle()
-        if not input_is_idle and batch.spec_info.bonus_tokens.numel() == 0:
+
+        if not input_is_idle and draft_extend_input.input_ids.numel() == 0:
+            # All reqs finished; stash an idle FrozenKVMTPDraftInput so the
+            # next-iter draft sees a valid spec_info.
             batch = batch.copy()
             batch.prepare_for_idle()
             batch.spec_info = FrozenKVMTPDraftInput.create_idle_input(
@@ -518,29 +524,32 @@ class FrozenKVMTPWorker(TpModelWorker):
                 topk=self.topk,
                 capture_hidden_mode=CaptureHiddenMode.LAST,
             )
+            return
 
         if batch.forward_mode.is_idle():
             return
 
-        draft_input = batch.spec_info
         seq_lens_backup = batch.seq_lens.clone()
         seq_lens_cpu_backup = batch.seq_lens_cpu.clone()
         req_pool_indices_backup = batch.req_pool_indices
 
         try:
             # Verify may leave finished requests in ScheduleBatch; seed only
-            # the unfinished requests carried by `verify_output`.
-            batch.seq_lens = verify_output.seq_lens_for_draft_extend
-            batch.seq_lens_cpu = verify_output.seq_lens_for_draft_extend_cpu
-            batch.req_pool_indices = verify_output.req_pool_indices_for_draft_extend
+            # the unfinished reqs carried by `draft_extend_input`.
+            batch.seq_lens = draft_extend_input.seq_lens
+            batch.seq_lens_cpu = draft_extend_input.seq_lens_cpu
+            batch.req_pool_indices = draft_extend_input.req_pool_indices
 
-            last_token_ids, last_hidden = self._select_last_verified_seed(draft_input)
+            last_token_ids, last_hidden = self._select_last_verified_seed(
+                draft_extend_input
+            )
+            # `_run_assistant_seed_step` constructs a fresh `FrozenKVMTPDraftInput`
+            # and installs it on `batch.spec_info` for next iter.
             self._run_assistant_seed_step(
                 batch,
                 last_token_ids,
                 last_hidden,
-                seq_lens_cpu=verify_output.seq_lens_for_draft_extend_cpu,
-                draft_input=draft_input,
+                seq_lens_cpu=draft_extend_input.seq_lens_cpu,
             )
         finally:
             batch.seq_lens = seq_lens_backup
@@ -687,7 +696,8 @@ class FrozenKVMTPWorker(TpModelWorker):
             score_list, token_list, parents_list, self.speculative_num_draft_tokens
         )
 
-    def verify(self, batch: ScheduleBatch, spec_info: FrozenKVMTPVerifyInput):
+    def verify(self, batch: ScheduleBatch):
+        spec_info: FrozenKVMTPVerifyInput = batch.spec_info
         seq_lens_pre_verify = batch.seq_lens.clone()
         spec_info.prepare_for_verify(batch, self.page_size)
         spec_info.num_tokens_per_req = self.speculative_num_steps + 1
@@ -697,7 +707,6 @@ class FrozenKVMTPWorker(TpModelWorker):
             if not batch.forward_mode.is_idle()
             else ForwardMode.IDLE
         )
-        batch.spec_info = spec_info
 
         model_worker_batch = batch.get_model_worker_batch(
             seq_lens_cpu_cache=spec_info.seq_lens_cpu
@@ -766,7 +775,6 @@ class FrozenKVMTPWorker(TpModelWorker):
         batch.forward_mode = (
             ForwardMode.DECODE if not batch.forward_mode.is_idle() else ForwardMode.IDLE
         )
-        batch.spec_info = res.next_draft_input
 
         del seq_lens_pre_verify
         return logits_output, res, can_run_cuda_graph

python/sglang/srt/speculative/multi_layer_eagle_draft_extend_cuda_graph_runner.py

@@ -41,7 +41,7 @@ from sglang.srt.model_executor.forward_batch_info import (
     ForwardMode,
 )
 from sglang.srt.model_executor.input_buffers import ForwardInputBuffers
-from sglang.srt.speculative.eagle_info import EagleDraftInput
+from sglang.srt.speculative.eagle_info import EagleDraftExtendInput
 from sglang.srt.speculative.multi_layer_eagle_utils import assign_new_state_triton
 from sglang.srt.speculative.spec_utils import fast_topk
 from sglang.srt.utils import (
@@ -349,7 +349,7 @@ class MultiLayerEagleDraftExtendCudaGraphRunner:
         else:
             global_dp_buffer_len = None
 
-        spec_info = EagleDraftInput(
+        spec_info = EagleDraftExtendInput(
             hidden_states=hidden_states,
             num_accepted_drafts=num_accepted_drafts,
             num_accepted_tokens=num_accepted_tokens,

python/sglang/srt/speculative/multi_layer_eagle_worker.py

@@ -34,6 +34,7 @@ from sglang.srt.model_executor.forward_batch_info import (
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.speculative.draft_utils import DraftBackendFactory
 from sglang.srt.speculative.eagle_info import (
+    EagleDraftExtendInput,
     EagleDraftInput,
     EagleVerifyInput,
     EagleVerifyOutput,
@@ -271,22 +272,33 @@ class MultiLayerEagleWorker(TpModelWorker):
             with self.draft_tp_context(
                 self.mtp_model_runner(0).tp_group
             ), speculative_moe_backend_context():
-                spec_info = self.draft(batch)
-            logits_output, verify_output, can_run_cuda_graph = self.verify(
-                batch, spec_info
-            )
+                verify_input = self.draft(batch)
+            batch.spec_info = verify_input
+            logits_output, verify_output, can_run_cuda_graph = self.verify(batch)
 
             with self.draft_tp_context(
                 self.mtp_model_runner(0).tp_group
             ), speculative_moe_backend_context():
                 # NOTE: We should use `check_forward_draft_extend_after_decode`
                 # when DP attention is enabled, but it is slow. Skip it for now.
+                draft_extend_input = verify_output.draft_extend_input
                 if (
                     self.server_args.enable_dp_attention
-                    or verify_output.unfinished_accept_tokens.shape[0] > 0
+                    or draft_extend_input.input_ids.shape[0] > 0
                 ):
-                    # decode is not finished
-                    self.forward_draft_extend_after_decode(batch, verify_output)
+                    # decode is not finished; stash for extend, then restash
+                    # the next-iter EagleDraftInput it returns.
+                    batch.spec_info = draft_extend_input
+                    next_draft_input = self.forward_draft_extend_after_decode(batch)
+                    batch.spec_info = next_draft_input
+                else:
+                    # All reqs finished and dp_attention isn't forcing extend.
+                    # Stash an empty EagleDraftInput so next iter's merge_batch
+                    # short-circuits on None hidden_states (EagleVerifyInput
+                    # has no merge_batch).
+                    batch.spec_info = EagleDraftInput(
+                        capture_hidden_mode=CaptureHiddenMode.LAST,
+                    )
 
             return GenerationBatchResult(
                 logits_output=logits_output,
@@ -296,7 +308,7 @@ class MultiLayerEagleWorker(TpModelWorker):
             )
 
     def check_forward_draft_extend_after_decode(self, verify_output: EagleVerifyOutput):
-        local_need_forward = verify_output.unfinished_accept_tokens.shape[0] > 0
+        local_need_forward = verify_output.draft_extend_input.input_ids.shape[0] > 0
         if not self.server_args.enable_dp_attention:
             return local_need_forward
 
@@ -471,7 +483,8 @@ class MultiLayerEagleWorker(TpModelWorker):
         # allocator and kv cache pool are shared with target worker
         pass
 
-    def verify(self, batch: ScheduleBatch, spec_info: EagleVerifyInput):
+    def verify(self, batch: ScheduleBatch):
+        spec_info: EagleVerifyInput = batch.spec_info
         spec_info.prepare_for_verify(batch, self.page_size)
         batch.return_hidden_states = False
         batch.forward_mode = (
@@ -479,7 +492,6 @@ class MultiLayerEagleWorker(TpModelWorker):
             if not batch.forward_mode.is_idle()
             else ForwardMode.IDLE
         )
-        batch.spec_info = spec_info
 
         model_worker_batch = batch.get_model_worker_batch(
             seq_lens_cpu_cache=spec_info.seq_lens_cpu
@@ -589,7 +601,6 @@ class MultiLayerEagleWorker(TpModelWorker):
         batch.forward_mode = (
             ForwardMode.DECODE if not batch.forward_mode.is_idle() else ForwardMode.IDLE
         )
-        batch.spec_info = res.next_draft_input
 
         return logits_output, res, can_run_cuda_graph
 
@@ -653,20 +664,19 @@ class MultiLayerEagleWorker(TpModelWorker):
         forward_batch.spec_info.topk_index = torch.cat(topk_index_list, dim=1)
 
     def forward_draft_extend_after_decode(
-        self, batch: ScheduleBatch, verify_output: EagleVerifyOutput
-    ):
-        assert isinstance(batch.spec_info, EagleDraftInput)
+        self, batch: ScheduleBatch
+    ) -> EagleDraftInput:
+        draft_extend_input: EagleDraftExtendInput = batch.spec_info
+
         # Backup fields that will be modified in-place
         seq_lens_backup = batch.seq_lens.clone()
         seq_lens_cpu_backup = batch.seq_lens_cpu.clone()
         req_pool_indices_backup = batch.req_pool_indices
-        num_accepted_drafts_backup = batch.spec_info.num_accepted_drafts
-        num_accepted_tokens_backup = batch.spec_info.num_accepted_tokens
         return_logprob_backup = batch.return_logprob
 
         input_is_idle = batch.forward_mode.is_idle()
 
-        if not input_is_idle and verify_output.unfinished_accept_tokens.numel() == 0:
+        if not input_is_idle and draft_extend_input.input_ids.numel() == 0:
             batch = batch.copy()
             batch.prepare_for_idle()
             hidden_size = (
@@ -674,19 +684,19 @@ class MultiLayerEagleWorker(TpModelWorker):
                 if self.speculative_algorithm.is_eagle3()
                 else self.model_config.hidden_size
             )
-            batch.spec_info = EagleDraftInput.create_idle_input(
+            draft_extend_input = EagleDraftExtendInput.create_idle_input(
                 device=self.device,
                 hidden_size=hidden_size,
                 dtype=self.model_config.dtype,
-                topk=self.topk,
                 capture_hidden_mode=CaptureHiddenMode.LAST,
             )
+            batch.spec_info = draft_extend_input
 
-        batch.spec_info.num_tokens_per_req = self.speculative_num_steps + 1
-        batch.spec_info.num_tokens_for_logprob_per_req = 1
-        batch.spec_info.prepare_extend_after_decode(
+        # Phase 1: prepare extend (kernel writes draft_extend_input.{positions, bonus_tokens})
+        draft_extend_input.num_tokens_per_req = self.speculative_num_steps + 1
+        draft_extend_input.num_tokens_for_logprob_per_req = 1
+        draft_extend_input.prepare_extend_after_decode(
             batch,
-            verify_output=verify_output,
             speculative_num_steps=self.speculative_num_steps,
         )
         batch.forward_mode = (
@@ -748,17 +758,23 @@ class MultiLayerEagleWorker(TpModelWorker):
                     )
                     pt += extend_len
 
-        forward_batch.spec_info.topk_p = torch.cat(topk_p_list, dim=1)
-        forward_batch.spec_info.topk_index = torch.cat(topk_index_list, dim=1)
+        # Phase 3: assemble next-iter EagleDraftInput from extend output
+        next_draft_input = EagleDraftInput(
+            bonus_tokens=draft_extend_input.bonus_tokens,
+            hidden_states=logits_output.hidden_states,
+            topk_p=torch.cat(topk_p_list, dim=1),
+            topk_index=torch.cat(topk_index_list, dim=1),
+            capture_hidden_mode=CaptureHiddenMode.FULL,
+        )
 
-        # Restore backup.
-        # This is because `seq_lens` can be modified in `prepare_extend_after_decode`
+        # Restore batch fields. `seq_lens` etc. were modified by
+        # `prepare_extend_after_decode`. Caller installs `next_draft_input` as
+        # `batch.spec_info`.
         batch.forward_mode = (
             ForwardMode.DECODE if not input_is_idle else ForwardMode.IDLE
         )
         batch.seq_lens = seq_lens_backup
         batch.seq_lens_cpu = seq_lens_cpu_backup
         batch.req_pool_indices = req_pool_indices_backup
-        batch.spec_info.num_accepted_drafts = num_accepted_drafts_backup
-        batch.spec_info.num_accepted_tokens = num_accepted_tokens_backup
         batch.return_logprob = return_logprob_backup
+        return next_draft_input

python/sglang/srt/speculative/spec_info.py

@@ -195,8 +195,10 @@ class SpecInputType(IntEnum):
     # NOTE: introduce this to distinguish the SpecInput types of multiple algorithms when asserting in attention backends.
     # If all algorithms can share the same datastrucutre of draft_input and verify_input, consider simplify it
     EAGLE_DRAFT = auto()
+    EAGLE_DRAFT_EXTEND = auto()
     EAGLE_VERIFY = auto()
     FROZEN_KV_MTP_DRAFT = auto()
+    FROZEN_KV_MTP_DRAFT_EXTEND = auto()
     FROZEN_KV_MTP_VERIFY = auto()
     DFLASH_DRAFT = auto()
     DFLASH_VERIFY = auto()
@@ -212,7 +214,9 @@ class SpecInput(ABC):
         # or use another variable name like `draft_input` to substitute `spec_info`
         return self.spec_input_type in {
             SpecInputType.EAGLE_DRAFT,
+            SpecInputType.EAGLE_DRAFT_EXTEND,
             SpecInputType.FROZEN_KV_MTP_DRAFT,
+            SpecInputType.FROZEN_KV_MTP_DRAFT_EXTEND,
             SpecInputType.DFLASH_DRAFT,
         }