Unify forward output datastructure (#11124)

python/sglang/srt/configs/model_config.py

@@ -22,6 +22,7 @@ from typing import List, Optional, Set, Union
 import torch
 from transformers import PretrainedConfig
 
+from sglang.srt.environ import envs
 from sglang.srt.hf_transformers_utils import (
     get_config,
     get_context_length,
@@ -31,7 +32,7 @@ from sglang.srt.hf_transformers_utils import (
 )
 from sglang.srt.layers.quantization import QUANTIZATION_METHODS
 from sglang.srt.server_args import ServerArgs
-from sglang.srt.utils import get_bool_env_var, is_hip, retry
+from sglang.srt.utils import is_hip, retry
 from sglang.utils import is_in_ci
 
 logger = logging.getLogger(__name__)
@@ -237,7 +238,7 @@ class ModelConfig:
                     f"This may lead to incorrect model outputs or CUDA errors. Note that the derived context_length may differ from max_position_embeddings in the model's config."
                 )
                 if (
-                    get_bool_env_var("SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN")
+                    envs.SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN.get()
                     or is_in_ci()  # FIXME: fix this special case
                 ):
                     logger.warning(msg)

python/sglang/srt/disaggregation/prefill.py

@@ -689,7 +689,6 @@ class SchedulerDisaggregationPrefillMixin:
         self.running_mbs = [
             ScheduleBatch(reqs=[], batch_is_full=False) for _ in range(self.pp_size)
         ]
-        bids = [None] * self.pp_size
         pp_outputs: Optional[PPProxyTensors] = None
 
         # Either success or failed
@@ -761,10 +760,7 @@ class SchedulerDisaggregationPrefillMixin:
                 # send the outputs to the next step
                 if self.pp_group.is_last_rank:
                     if self.cur_batch:
-                        next_token_ids, bids[mb_id] = (
-                            result.next_token_ids,
-                            result.bid,
-                        )
+                        next_token_ids = result.next_token_ids
                         pp_outputs = PPProxyTensors(
                             {
                                 "next_token_ids": next_token_ids,
@@ -801,7 +797,6 @@ class SchedulerDisaggregationPrefillMixin:
                         next_token_ids=next_pp_outputs["next_token_ids"],
                         extend_input_len_per_req=None,
                         extend_logprob_start_len_per_req=None,
-                        bid=bids[next_mb_id],
                         can_run_cuda_graph=result.can_run_cuda_graph,
                     )
                     self.process_batch_result_disagg_prefill(
@@ -818,8 +813,6 @@ class SchedulerDisaggregationPrefillMixin:
 
                 # carry the outputs to the next stage
                 if not self.pp_group.is_last_rank:
-                    if self.cur_batch:
-                        bids[mb_id] = result.bid
                     if pp_outputs:
                         # send the outputs from the last round to let the next stage worker run post processing
                         self.pp_group.send_tensor_dict(
@@ -838,8 +831,10 @@ class SchedulerDisaggregationPrefillMixin:
 
                     # send out proxy tensors to the next stage
                     if self.cur_batch:
+                        # FIXME(lsyin): remove this assert
+                        assert result.pp_hidden_states_proxy_tensors.tensors is not None
                         self.pp_group.send_tensor_dict(
-                            result.pp_hidden_states_proxy_tensors,
+                            result.pp_hidden_states_proxy_tensors.tensors,
                             all_gather_group=self.attn_tp_group,
                         )
 

python/sglang/srt/managers/schedule_batch.py

@@ -860,10 +860,6 @@ class Req:
         )
 
 
-# Batch id
-bid = 0
-
-
 @dataclasses.dataclass
 class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
     """Store all information of a batch on the scheduler."""
@@ -1829,10 +1825,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             seq_lens_cpu_cache if seq_lens_cpu_cache is not None else self.seq_lens_cpu
         )
 
-        global bid
-        bid += 1
         return ModelWorkerBatch(
-            bid=bid,
             forward_mode=self.forward_mode,
             input_ids=self.input_ids,
             req_pool_indices=self.req_pool_indices,
@@ -1952,8 +1945,6 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
 
 @dataclasses.dataclass
 class ModelWorkerBatch:
-    # The batch id
-    bid: int
     # The forward mode
     forward_mode: ForwardMode
     # The input ids

python/sglang/srt/managers/scheduler.py

@@ -150,7 +150,11 @@ from sglang.srt.mem_cache.chunk_cache import ChunkCache, SWAChunkCache
 from sglang.srt.mem_cache.hiradix_cache import HiRadixCache
 from sglang.srt.mem_cache.radix_cache import RadixCache
 from sglang.srt.mem_cache.swa_radix_cache import SWARadixCache
-from sglang.srt.model_executor.forward_batch_info import ForwardMode, PPProxyTensors
+from sglang.srt.model_executor.forward_batch_info import (
+    ForwardBatchOutput,
+    ForwardMode,
+    PPProxyTensors,
+)
 from sglang.srt.parser.reasoning_parser import ReasoningParser
 from sglang.srt.server_args import PortArgs, ServerArgs
 from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
@@ -175,7 +179,6 @@ from sglang.srt.utils import (
     get_bool_env_var,
     get_int_env_var,
     get_zmq_socket,
-    is_cpu,
     kill_itself_when_parent_died,
     numa_bind_to_node,
     point_to_point_pyobj,
@@ -194,24 +197,59 @@ logger = logging.getLogger(__name__)
 TEST_RETRACT = get_bool_env_var("SGLANG_TEST_RETRACT")
 GRAMMAR_TIMEOUT = float(os.environ.get("SGLANG_GRAMMAR_TIMEOUT", 300))
 
-_is_cpu = is_cpu()
-
 
 @dataclass
 class GenerationBatchResult:
     logits_output: Optional[LogitsProcessorOutput]
-    pp_hidden_states_proxy_tensors: Optional[torch.Tensor]
+    pp_hidden_states_proxy_tensors: Optional[PPProxyTensors]
     next_token_ids: Optional[List[int]]
+    can_run_cuda_graph: bool
+
+    # For output processing
     extend_input_len_per_req: List[int]
     extend_logprob_start_len_per_req: List[int]
-    bid: int
-    can_run_cuda_graph: bool
+
+    @classmethod
+    def from_forward_batch_output(
+        cls,
+        forward_batch_output: ForwardBatchOutput,
+        extend_input_len_per_req: List[int],
+        extend_logprob_start_len_per_req: List[int],
+    ):
+        # TODO(lsyin): remove this workaround logic and try to unify output classes
+
+        return cls(
+            logits_output=forward_batch_output.logits_output,
+            pp_hidden_states_proxy_tensors=forward_batch_output.pp_proxy_tensors,
+            next_token_ids=forward_batch_output.next_token_ids,
+            extend_input_len_per_req=extend_input_len_per_req,
+            extend_logprob_start_len_per_req=extend_logprob_start_len_per_req,
+            can_run_cuda_graph=forward_batch_output.can_run_cuda_graph,
+        )
+
+    @classmethod
+    def from_pp_proxy(
+        cls, logits_output, next_pp_outputs: PPProxyTensors, can_run_cuda_graph
+    ):
+        # TODO(lsyin): also simplify this logic
+        # Current PP implementation in scheduler is not compatible with ForwardBatchOutput
+        # Maybe introduce a ProxyBatchOutput for PP and the original ForwardBatchOutput for TP
+        proxy_dict = next_pp_outputs.tensors
+        return cls(
+            logits_output=logits_output,
+            pp_hidden_states_proxy_tensors=None,
+            next_token_ids=next_pp_outputs["next_token_ids"],
+            extend_input_len_per_req=proxy_dict.get("extend_input_len_per_req", None),
+            extend_logprob_start_len_per_req=proxy_dict.get(
+                "extend_logprob_start_len_per_req", None
+            ),
+            can_run_cuda_graph=can_run_cuda_graph,
+        )
 
 
 @dataclass
 class EmbeddingBatchResult:
     embeddings: torch.Tensor
-    bid: int
 
 
 class Scheduler(
@@ -403,6 +441,12 @@ class Scheduler(
         else:
             self.draft_worker = None
 
+        # Dispatch the model worker
+        if self.spec_algorithm.is_none():
+            self.model_worker = self.tp_worker
+        else:
+            self.model_worker = self.draft_worker
+
         # Get token and memory info from the model worker
         (
             self.max_total_num_tokens,
@@ -959,7 +1003,6 @@ class Scheduler(
         self.running_mbs = [
             ScheduleBatch(reqs=[], batch_is_full=False) for _ in range(self.pp_size)
         ]
-        bids = [None] * self.pp_size
         pp_outputs: Optional[PPProxyTensors] = None
         while True:
             server_is_idle = True
@@ -980,10 +1023,7 @@ class Scheduler(
                 # (last rank) send the outputs to the next step
                 if self.pp_group.is_last_rank:
                     if self.cur_batch:
-                        next_token_ids, bids[mb_id] = (
-                            result.next_token_ids,
-                            result.bid,
-                        )
+                        next_token_ids = result.next_token_ids
                         if self.cur_batch.return_logprob:
                             pp_outputs = PPProxyTensors(
                                 {
@@ -1031,17 +1071,10 @@ class Scheduler(
                         logits_output = LogitsProcessorOutput(**logits_output_args)
                     else:
                         logits_output = None
-                    output_result = GenerationBatchResult(
+
+                    output_result = GenerationBatchResult.from_pp_proxy(
                         logits_output=logits_output,
-                        pp_hidden_states_proxy_tensors=None,
-                        next_token_ids=next_pp_outputs["next_token_ids"],
-                        extend_input_len_per_req=next_pp_outputs.tensors.get(
-                            "extend_input_len_per_req", None
-                        ),
-                        extend_logprob_start_len_per_req=next_pp_outputs.tensors.get(
-                            "extend_logprob_start_len_per_req", None
-                        ),
-                        bid=bids[next_mb_id],
+                        next_pp_outputs=next_pp_outputs,
                         can_run_cuda_graph=result.can_run_cuda_graph,
                     )
                     self.process_batch_result(mbs[next_mb_id], output_result)
@@ -1049,8 +1082,6 @@ class Scheduler(
 
                 # (not last rank)
                 if not self.pp_group.is_last_rank:
-                    if self.cur_batch:
-                        bids[mb_id] = result.bid
                     # carry the outputs to the next stage
                     # send the outputs from the last round to let the next stage worker run post processing
                     if pp_outputs:
@@ -1072,8 +1103,10 @@ class Scheduler(
 
                     # send out proxy tensors to the next stage
                     if self.cur_batch:
+                        # FIXME(lsyin): remove this assert
+                        assert result.pp_hidden_states_proxy_tensors.tensors is not None
                         self.pp_group.send_tensor_dict(
-                            result.pp_hidden_states_proxy_tensors,
+                            result.pp_hidden_states_proxy_tensors.tensors,
                             all_gather_group=self.attn_tp_group,
                         )
 
@@ -2016,33 +2049,25 @@ class Scheduler(
 
         # Run forward
         if self.is_generation:
+
+            batch_or_worker_batch = batch
+
             if self.spec_algorithm.is_none():
-                model_worker_batch = batch.get_model_worker_batch()
+                # FIXME(lsyin): remove this if and finally unify the abstraction
+                batch_or_worker_batch = batch.get_model_worker_batch()
 
-                if self.pp_group.is_last_rank:
-                    logits_output, next_token_ids, can_run_cuda_graph = (
-                        self.tp_worker.forward_batch_generation(model_worker_batch)
-                    )
-                else:
-                    pp_hidden_states_proxy_tensors, _, can_run_cuda_graph = (
-                        self.tp_worker.forward_batch_generation(model_worker_batch)
-                    )
-                bid = model_worker_batch.bid
-            else:
-                (
-                    logits_output,
-                    next_token_ids,
-                    bid,
-                    num_accepted_tokens,
-                    can_run_cuda_graph,
-                ) = self.draft_worker.forward_batch_speculative_generation(batch)
-                bs = batch.batch_size()
-                self.spec_num_total_accepted_tokens += num_accepted_tokens + bs
-                self.spec_num_total_forward_ct += bs
-                self.num_generated_tokens += num_accepted_tokens
-
-            if self.pp_group.is_last_rank:
-                batch.output_ids = next_token_ids
+            forward_batch_output = self.model_worker.forward_batch_generation(
+                batch_or_worker_batch
+            )
+
+            if not self.spec_algorithm.is_none():
+                # TODO(lsyin): unify this metric-updating logic with non-spec, and move it to decode processing
+                self.udpate_spec_metrics(
+                    batch.batch_size(), forward_batch_output.num_accepted_tokens
+                )
+
+            # update batch's output ids
+            batch.output_ids = forward_batch_output.next_token_ids
 
             # These 2 values are needed for processing the output, but the values can be
             # modified by overlap schedule. So we have to copy them here so that
@@ -2051,6 +2076,7 @@ class Scheduler(
                 extend_input_len_per_req = [req.extend_input_len for req in batch.reqs]
             else:
                 extend_input_len_per_req = None
+
             if batch.return_logprob:
                 extend_logprob_start_len_per_req = [
                     req.extend_logprob_start_len for req in batch.reqs
@@ -2058,25 +2084,15 @@ class Scheduler(
             else:
                 extend_logprob_start_len_per_req = None
 
-            ret = GenerationBatchResult(
-                logits_output=logits_output if self.pp_group.is_last_rank else None,
-                pp_hidden_states_proxy_tensors=(
-                    pp_hidden_states_proxy_tensors
-                    if not self.pp_group.is_last_rank
-                    else None
-                ),
-                next_token_ids=next_token_ids if self.pp_group.is_last_rank else None,
+            return GenerationBatchResult.from_forward_batch_output(
+                forward_batch_output=forward_batch_output,
                 extend_input_len_per_req=extend_input_len_per_req,
                 extend_logprob_start_len_per_req=extend_logprob_start_len_per_req,
-                bid=bid,
-                can_run_cuda_graph=can_run_cuda_graph,
             )
         else:  # embedding or reward model
             model_worker_batch = batch.get_model_worker_batch()
             embeddings = self.tp_worker.forward_batch_embedding(model_worker_batch)
-            ret = EmbeddingBatchResult(
-                embeddings=embeddings, bid=model_worker_batch.bid
-            )
+            ret = EmbeddingBatchResult(embeddings=embeddings)
         return ret
 
     def process_batch_result(

python/sglang/srt/managers/scheduler_metrics_mixin.py

@@ -80,6 +80,11 @@ class SchedulerMetricsMixin:
                 kv_events_config, self.attn_dp_rank
             )
 
+    def udpate_spec_metrics(self, bs: int, num_accepted_tokens: int):
+        self.spec_num_total_accepted_tokens += num_accepted_tokens + bs
+        self.spec_num_total_forward_ct += bs
+        self.num_generated_tokens += num_accepted_tokens
+
     def log_prefill_stats(
         self: Scheduler,
         adder: PrefillAdder,

python/sglang/srt/managers/scheduler_output_processor_mixin.py

@@ -173,8 +173,7 @@ class SchedulerOutputProcessorMixin:
             self.set_next_batch_sampling_info_done(batch)
 
         else:  # embedding or reward model
-            embeddings, bid = result.embeddings, result.bid
-            embeddings = embeddings.tolist()
+            embeddings = result.embeddings.tolist()
 
             # Check finish conditions
             for i, req in enumerate(batch.reqs):

python/sglang/srt/managers/tp_worker.py

@@ -43,7 +43,11 @@ from sglang.srt.managers.io_struct import (
 from sglang.srt.managers.schedule_batch import ModelWorkerBatch, global_server_args_dict
 from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
 from sglang.srt.mem_cache.memory_pool import ReqToTokenPool
-from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
+from sglang.srt.model_executor.forward_batch_info import (
+    ForwardBatch,
+    ForwardBatchOutput,
+    PPProxyTensors,
+)
 from sglang.srt.model_executor.model_runner import ModelRunner
 from sglang.srt.patch_torch import monkey_patch_torch_reductions
 from sglang.srt.server_args import ServerArgs
@@ -234,9 +238,7 @@ class TpModelWorker:
         model_worker_batch: ModelWorkerBatch,
         launch_done: Optional[threading.Event] = None,
         skip_sample: bool = False,
-    ) -> Tuple[
-        Union[LogitsProcessorOutput, torch.Tensor], Optional[torch.Tensor], bool
-    ]:
+    ) -> ForwardBatchOutput:
         # update the consumer index of hicache to the running batch
         self.set_hicache_consumer(model_worker_batch.hicache_consumer_index)
 
@@ -271,13 +273,20 @@ class TpModelWorker:
             else:
                 next_token_ids = self.model_runner.sample(logits_output, forward_batch)
 
-            return logits_output, next_token_ids, can_run_cuda_graph
+            return ForwardBatchOutput(
+                logits_output=logits_output,
+                next_token_ids=next_token_ids,
+                can_run_cuda_graph=can_run_cuda_graph,
+            )
         else:
             pp_proxy_tensors, can_run_cuda_graph = self.model_runner.forward(
                 forward_batch,
                 pp_proxy_tensors=pp_proxy_tensors,
             )
-            return pp_proxy_tensors.tensors, None, can_run_cuda_graph
+            return ForwardBatchOutput(
+                pp_proxy_tensors=pp_proxy_tensors,
+                can_run_cuda_graph=can_run_cuda_graph,
+            )
 
     def forward_batch_embedding(self, model_worker_batch: ModelWorkerBatch):
         forward_batch = ForwardBatch.init_new(model_worker_batch, self.model_runner)

python/sglang/srt/managers/tp_worker_overlap_thread.py

@@ -39,6 +39,7 @@ from sglang.srt.managers.io_struct import (
 from sglang.srt.managers.overlap_utils import FutureMap
 from sglang.srt.managers.schedule_batch import ModelWorkerBatch
 from sglang.srt.managers.tp_worker import TpModelWorker
+from sglang.srt.model_executor.forward_batch_info import ForwardBatchOutput
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import DynamicGradMode
 from sglang.utils import get_exception_traceback
@@ -160,13 +161,17 @@ class TpModelWorkerClient:
             self.future_map.resolve_future(model_worker_batch)
 
             # Run forward
+            forward_batch_output = self.worker.forward_batch_generation(
+                model_worker_batch,
+                model_worker_batch.launch_done,
+                #  Skip sampling for prefill-only requests
+                skip_sample=model_worker_batch.is_prefill_only,
+            )
+
             logits_output, next_token_ids, can_run_cuda_graph = (
-                self.worker.forward_batch_generation(
-                    model_worker_batch,
-                    model_worker_batch.launch_done,
-                    #  Skip sampling for prefill-only requests
-                    skip_sample=model_worker_batch.is_prefill_only,
-                )
+                forward_batch_output.logits_output,
+                forward_batch_output.next_token_ids,
+                forward_batch_output.can_run_cuda_graph,
             )
 
             # Update the future token ids map
@@ -227,7 +232,7 @@ class TpModelWorkerClient:
 
     def forward_batch_generation(
         self, model_worker_batch: ModelWorkerBatch
-    ) -> Tuple[None, torch.Tensor, bool]:
+    ) -> ForwardBatchOutput:
         # Create a new copy of sampling_info because it will be updated in-place by the scheduler for the next batch.
         sampling_info = model_worker_batch.sampling_info
         sampling_info.update_penalties()
@@ -250,7 +255,10 @@ class TpModelWorkerClient:
         future_next_token_ids = self.future_map.update_next_future(
             cur_future_map_ct, bs
         )
-        return None, future_next_token_ids, False
+        return ForwardBatchOutput(
+            next_token_ids=future_next_token_ids,
+            can_run_cuda_graph=False,
+        )
 
     def update_weights_from_disk(self, recv_req: UpdateWeightFromDiskReqInput):
         success, message = self.worker.update_weights_from_disk(recv_req)

python/sglang/srt/managers/utils.py

@@ -2,11 +2,10 @@ from __future__ import annotations
 
 import logging
 import multiprocessing as mp
-from http import HTTPStatus
 from typing import TYPE_CHECKING, Dict, List, Optional
 
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
-from sglang.srt.managers.schedule_batch import FINISH_ABORT, Req
+from sglang.srt.managers.schedule_batch import Req
 from sglang.srt.model_executor.forward_batch_info import PPProxyTensors
 
 if TYPE_CHECKING:

python/sglang/srt/model_executor/forward_batch_info.py

@@ -900,6 +900,17 @@ class ForwardBatch:
         return self.tbo_split_seq_index is not None
 
 
+@dataclass
+class ForwardBatchOutput:
+    # FIXME(lsyin): unify the forward batch output between different spec and parallelism
+    # need to be more organized
+    logits_output: Optional[torch.Tensor] = None
+    next_token_ids: Optional[torch.Tensor] = None
+    num_accepted_tokens: Optional[int] = None
+    pp_proxy_tensors: Optional[PPProxyTensors] = None
+    can_run_cuda_graph: bool = False
+
+
 def enable_num_token_non_padded(server_args):
     return get_moe_expert_parallel_world_size() > 1
 

python/sglang/srt/speculative/eagle_worker.py

@@ -14,7 +14,6 @@ from sglang.srt.distributed import (
 )
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
 from sglang.srt.layers.sampler import get_token_ids_logprobs, get_top_logprobs
-from sglang.srt.managers.mm_utils import embed_mm_inputs
 from sglang.srt.managers.schedule_batch import (
     ScheduleBatch,
     get_last_loc,
@@ -24,6 +23,7 @@ from sglang.srt.managers.tp_worker import TpModelWorker
 from sglang.srt.model_executor.forward_batch_info import (
     CaptureHiddenMode,
     ForwardBatch,
+    ForwardBatchOutput,
     ForwardMode,
 )
 from sglang.srt.server_args import ServerArgs
@@ -422,9 +422,7 @@ class EAGLEWorker(TpModelWorker):
     def draft_model_runner(self):
         return self.model_runner
 
-    def forward_batch_speculative_generation(
-        self, batch: ScheduleBatch
-    ) -> Tuple[LogitsProcessorOutput, torch.Tensor, int, int, bool]:
+    def forward_batch_generation(self, batch: ScheduleBatch) -> ForwardBatchOutput:
         """Run speculative decoding forward.
 
         NOTE: Many states of batch is modified as you go through. It is not guaranteed that
@@ -437,14 +435,19 @@ class EAGLEWorker(TpModelWorker):
             the batch id (used for overlap schedule), and number of accepted tokens.
         """
         if batch.forward_mode.is_extend() or batch.is_extend_in_batch:
-            logits_output, next_token_ids, bid, seq_lens_cpu = (
-                self.forward_target_extend(batch)
+            logits_output, next_token_ids, seq_lens_cpu = self.forward_target_extend(
+                batch
             )
             with self.draft_tp_context(self.draft_model_runner.tp_group):
                 self.forward_draft_extend(
                     batch, logits_output.hidden_states, next_token_ids, seq_lens_cpu
                 )
-            return logits_output, next_token_ids, bid, 0, False
+            return ForwardBatchOutput(
+                logits_output=logits_output,
+                next_token_ids=next_token_ids,
+                num_accepted_tokens=0,
+                can_run_cuda_graph=False,
+            )
         else:
             with self.draft_tp_context(self.draft_model_runner.tp_group):
                 spec_info = self.draft(batch)
@@ -462,12 +465,11 @@ class EAGLEWorker(TpModelWorker):
                     # decode is not finished
                     self.forward_draft_extend_after_decode(batch)
 
-            return (
-                logits_output,
-                verify_output.verified_id,
-                model_worker_batch.bid,
-                sum(verify_output.accept_length_per_req_cpu),
-                can_run_cuda_graph,
+            return ForwardBatchOutput(
+                logits_output=logits_output,
+                next_token_ids=verify_output.verified_id,
+                num_accepted_tokens=sum(verify_output.accept_length_per_req_cpu),
+                can_run_cuda_graph=can_run_cuda_graph,
             )
 
     def check_forward_draft_extend_after_decode(self, batch: ScheduleBatch):
@@ -499,19 +501,21 @@ class EAGLEWorker(TpModelWorker):
         Returns:
             logits_output: The output of logits. It will contain the full hidden states.
             next_token_ids: Next token ids generated.
-            bid: The model batch ID. Used for overlap schedule.
         """
         # Forward with the target model and get hidden states.
         # We need the full hidden states to prefill the KV cache of the draft model.
         model_worker_batch = batch.get_model_worker_batch()
         model_worker_batch.capture_hidden_mode = CaptureHiddenMode.FULL
-        logits_output, next_token_ids, _ = self.target_worker.forward_batch_generation(
+        forward_batch_output = self.target_worker.forward_batch_generation(
             model_worker_batch
         )
+        logits_output, next_token_ids = (
+            forward_batch_output.logits_output,
+            forward_batch_output.next_token_ids,
+        )
         return (
             logits_output,
             next_token_ids,
-            model_worker_batch.bid,
             model_worker_batch.seq_lens_cpu,
         )
 
@@ -811,10 +815,12 @@ class EAGLEWorker(TpModelWorker):
             ).cpu()
 
         # Forward
-        logits_output, _, can_run_cuda_graph = (
-            self.target_worker.forward_batch_generation(
-                model_worker_batch, skip_sample=True
-            )
+        forward_batch_output = self.target_worker.forward_batch_generation(
+            model_worker_batch, skip_sample=True
+        )
+        logits_output, can_run_cuda_graph = (
+            forward_batch_output.logits_output,
+            forward_batch_output.can_run_cuda_graph,
         )
 
         vocab_mask = None

python/sglang/srt/speculative/ngram_worker.py

@@ -7,7 +7,7 @@ from sgl_kernel.speculative import reconstruct_indices_from_tree_mask
 
 from sglang.srt.managers.schedule_batch import ScheduleBatch
 from sglang.srt.managers.tp_worker import TpModelWorker
-from sglang.srt.model_executor.forward_batch_info import ForwardMode
+from sglang.srt.model_executor.forward_batch_info import ForwardBatchOutput, ForwardMode
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.speculative.cpp_ngram.ngram_cache import NgramCache
 from sglang.srt.speculative.ngram_utils import NgramVerifyInput
@@ -207,17 +207,18 @@ class NGRAMWorker:
             batch_tokens.append(put_ids)
         self.ngram_cache.batch_put(batch_tokens)
 
-    def forward_batch_speculative_generation(self, batch: ScheduleBatch):
+    def forward_batch_generation(self, batch: ScheduleBatch) -> ForwardBatchOutput:
         self._prepare_for_speculative_decoding(batch)
         model_worker_batch = batch.get_model_worker_batch()
-        bid = model_worker_batch.bid
         num_accepted_tokens = 0
 
         if model_worker_batch.forward_mode.is_target_verify():
-            logits_output, _, can_run_cuda_graph = (
-                self.target_worker.forward_batch_generation(
-                    model_worker_batch, skip_sample=True
-                )
+            forward_batch_output = self.target_worker.forward_batch_generation(
+                model_worker_batch, skip_sample=True
+            )
+            logits_output, can_run_cuda_graph = (
+                forward_batch_output.logits_output,
+                forward_batch_output.can_run_cuda_graph,
             )
             verify_input = model_worker_batch.spec_info
             logits_output, next_token_ids, num_accepted_tokens = verify_input.verify(
@@ -227,14 +228,18 @@ class NGRAMWorker:
             batch.forward_mode = ForwardMode.DECODE
 
         else:
+            forward_batch_output = self.target_worker.forward_batch_generation(
+                model_worker_batch
+            )
             logits_output, next_token_ids, can_run_cuda_graph = (
-                self.target_worker.forward_batch_generation(model_worker_batch)
+                forward_batch_output.logits_output,
+                forward_batch_output.next_token_ids,
+                forward_batch_output.can_run_cuda_graph,
             )
 
-        return (
-            logits_output,
-            next_token_ids,
-            bid,
-            num_accepted_tokens,
-            can_run_cuda_graph,
+        return ForwardBatchOutput(
+            logits_output=logits_output,
+            next_token_ids=next_token_ids,
+            num_accepted_tokens=num_accepted_tokens,
+            can_run_cuda_graph=can_run_cuda_graph,
         )