[Spec] Single-source num_tokens_per_req derivation and access (#31013)

python/sglang/srt/layers/attention/flashattention_backend.py

@@ -30,6 +30,7 @@ from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMo
 from sglang.srt.runtime_context import get_server_args
 from sglang.srt.speculative.ragged_verify import build_ragged_target_verify_geometry
 from sglang.srt.speculative.spec_info import SpecInput, SpeculativeAlgorithm
+from sglang.srt.speculative.spec_utils import resolve_num_tokens_per_req
 from sglang.srt.utils import get_compiler_backend
 
 if TYPE_CHECKING:
@@ -191,10 +192,15 @@ class FlashAttentionBackend(AttentionBackend):
             self.speculative_num_draft_tokens is not None
             and model_runner.is_draft_worker
         ):
-            self.speculative_num_draft_tokens = SpeculativeAlgorithm.from_string(
-                model_runner.server_args.speculative_algorithm
-            ).get_num_tokens_per_req_for_target_verify(
-                int(self.speculative_num_draft_tokens), is_draft_worker=True
+            # Static verify width; NOTE: overwrites the config-named attr in place.
+            self.speculative_num_draft_tokens = resolve_num_tokens_per_req(
+                phase="target_verify",
+                server_args=model_runner.server_args,
+                spec_algorithm=SpeculativeAlgorithm.from_string(
+                    model_runner.server_args.speculative_algorithm
+                ),
+                is_draft_worker=True,
+                num_draft_tokens=int(self.speculative_num_draft_tokens),
             )
         self.speculative_step_id = speculative_step_id
 
@@ -2289,7 +2295,7 @@ class FlashAttentionBackend(AttentionBackend):
                     metadata.swa_spec_metadata = metadata_swa
 
         elif forward_mode.is_draft_extend_v2():
-            num_tokens_per_req = num_tokens // bs
+            num_tokens_per_req = spec_info.num_tokens_per_req
             metadata.cache_seqlens_int32 = self.draft_extend_metadata["cache_seqlens"][
                 :bs
             ]
@@ -2693,9 +2699,7 @@ class FlashAttentionBackend(AttentionBackend):
                     device=device,
                 )
             else:
-                default_extend = getattr(
-                    spec_info, "num_tokens_per_req", self.speculative_num_steps + 1
-                )
+                default_extend = spec_info.num_tokens_per_req
                 extend_seq_lens = torch.full(
                     (bs,), default_extend, dtype=torch.int32, device=device
                 )
@@ -2704,9 +2708,7 @@ class FlashAttentionBackend(AttentionBackend):
             if extend_seq_lens_cpu:
                 metadata.max_seq_len_q = int(max(extend_seq_lens_cpu))
             else:
-                metadata.max_seq_len_q = getattr(
-                    spec_info, "num_tokens_per_req", self.speculative_num_steps + 1
-                )
+                metadata.max_seq_len_q = spec_info.num_tokens_per_req
 
             metadata.cu_seqlens_q[1:].copy_(
                 torch.cumsum(extend_seq_lens, dim=0, dtype=torch.int32)

python/sglang/srt/layers/attention/flashinfer_backend.py

@@ -1937,7 +1937,9 @@ class FlashInferIndicesUpdaterPrefill:
         # host-known qo/kv layout from the caller. Assert rather than silently
         # fall back to plan()'s blocking D2H on the replay hot-path.
         paged_plan_kwargs = {}
-        num_tokens_per_req = getattr(spec_info, "num_tokens_per_req", None)
+        num_tokens_per_req = (
+            spec_info.num_tokens_per_req if spec_info is not None else None
+        )
         uses_fast_prefill = (
             hasattr(wrapper_paged.begin_forward, "func")
             and wrapper_paged.begin_forward.func is fast_prefill_plan

python/sglang/srt/layers/attention/trtllm_mha_backend.py

@@ -487,7 +487,7 @@ class TRTLLMHAAttnBackend(FlashInferAttnBackend):
             )
             self.target_verify_metadata[bs] = metadata
         elif forward_mode.is_draft_extend_v2():
-            num_tokens_per_req = num_tokens // bs
+            num_tokens_per_req = spec_info.num_tokens_per_req
             metadata.cache_seqlens_int32 = self.draft_extend_metadata["cache_seqlens"][
                 :bs
             ]

python/sglang/srt/model_executor/cpu_graph_runner.py

@@ -581,6 +581,7 @@ class CPUGraphRunner:
 
         self.capture_forward_mode = ForwardMode.DECODE
         self.capture_hidden_mode = CaptureHiddenMode.NULL
+        # Static capture width: CPU graphs are decode-only.
         self.num_tokens_per_req = 1
 
         # If returning hidden states is enabled, set initial capture hidden mode to full to avoid double-capture on startup

python/sglang/srt/model_executor/model_runner.py

@@ -163,6 +163,7 @@ from sglang.srt.server_args import (  # noqa: F401  (re-export)
     set_global_server_args_for_scheduler,
 )
 from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
+from sglang.srt.speculative.spec_utils import resolve_num_tokens_per_req
 from sglang.srt.state_capturer.base import TopkCaptureOutput
 from sglang.srt.state_capturer.indexer_topk import (
     create_indexer_capturer,
@@ -622,10 +623,12 @@ class ModelRunner:
     ) -> int:
         """Logits rows per decode batch slot."""
         if self.spec_algorithm.is_speculative():
-            if num_draft_tokens is None:
-                num_draft_tokens = self.server_args.speculative_num_draft_tokens
-            return self.spec_algorithm.get_num_tokens_per_req_for_target_verify(
-                num_draft_tokens, self.is_draft_worker
+            return resolve_num_tokens_per_req(
+                phase="target_verify",
+                server_args=self.server_args,
+                spec_algorithm=self.spec_algorithm,
+                is_draft_worker=self.is_draft_worker,
+                num_draft_tokens=num_draft_tokens,
             )
         dllm_config = DllmConfig.from_server_args(self.server_args)
         return dllm_config.block_size if dllm_config is not None else 1

python/sglang/srt/model_executor/model_runner_components/cuda_graph_setup.py

@@ -269,12 +269,7 @@ def capture_decode_graph(*, model_runner: ModelRunner) -> DecodeGraphCapture:
     role = "draft" if model_runner.is_draft_worker else "target"
     if model_runner.spec_algorithm.is_speculative():
         capture_name = f"{role} verify"
-        num_tokens_per_req = (
-            model_runner.spec_algorithm.get_num_tokens_per_req_for_target_verify(
-                model_runner.server_args.speculative_num_draft_tokens,
-                model_runner.is_draft_worker,
-            )
-        )
+        num_tokens_per_req = model_runner.decode_num_tokens_per_req()
     else:
         capture_name = f"{role} decode"
         num_tokens_per_req = 1

python/sglang/srt/model_executor/runner/base_runner.py

@@ -360,11 +360,7 @@ class BaseRunner(ABC):
                 if not mr.spec_algorithm.supports_target_verify_for_draft():
                     raise RuntimeError("This should not happen")
             capture_forward_mode = ForwardMode.TARGET_VERIFY
-            num_tokens_per_req = (
-                mr.spec_algorithm.get_num_tokens_per_req_for_target_verify(
-                    mr.server_args.speculative_num_draft_tokens, mr.is_draft_worker
-                )
-            )
+            num_tokens_per_req = mr.decode_num_tokens_per_req()
 
         if mr.server_args.enable_return_hidden_states:
             capture_hidden_mode = CaptureHiddenMode.FULL

python/sglang/srt/model_executor/runner/decode_cuda_graph_runner.py

@@ -254,6 +254,7 @@ class DecodeCudaGraphRunner(BaseCudaGraphRunner):
         # --- capture mode + tokens-per-bs ------------------------------
         self.capture_forward_mode = ForwardMode.DECODE
         self.capture_hidden_mode = CaptureHiddenMode.NULL
+        # Static capture width.
         self.num_tokens_per_req = model_runner.decode_num_tokens_per_req(
             num_draft_tokens=self.speculative_num_draft_tokens
         )

python/sglang/srt/model_executor/runner/eager_runner.py

@@ -83,10 +83,8 @@ class EagerRunner(BaseRunner):
                     ),
                 )
             else:
-                num_tokens_per_req = (
-                    mr.spec_algorithm.get_num_tokens_per_req_for_target_verify(
-                        num_draft_tokens, mr.is_draft_worker
-                    )
+                num_tokens_per_req = mr.decode_num_tokens_per_req(
+                    num_draft_tokens=num_draft_tokens
                 )
         else:
             dllm_config = DllmConfig.from_server_args(sa)
@@ -150,11 +148,7 @@ class EagerRunner(BaseRunner):
         mr = self.model_runner
         num_tokens_per_req = 1
         if mr.spec_algorithm.is_speculative():
-            num_tokens_per_req = (
-                mr.spec_algorithm.get_num_tokens_per_req_for_target_verify(
-                    mr.server_args.speculative_num_draft_tokens, mr.is_draft_worker
-                )
-            )
+            num_tokens_per_req = mr.decode_num_tokens_per_req()
         return (
             self._alloc_dummy_decode_buffers(
                 self._eager_max_bs, num_tokens_per_req=num_tokens_per_req

python/sglang/srt/speculative/base_spec_worker.py

@@ -283,6 +283,7 @@ class EagleDraftWorkerBase(ABC):
                 )
 
         # Get a forward batch
+        # Actual width of the next draft-decode forward: topk tokens per req.
         draft_input.num_tokens_per_req = topk
         draft_input.num_tokens_for_logprob_per_req = topk
         capture_mode = (

python/sglang/srt/speculative/dflash_info.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Optional, Tuple
+from typing import TYPE_CHECKING, Optional
 
 import torch
 
@@ -48,9 +48,7 @@ class DFlashVerifyInput(SpecInput):
         super().__init__(spec_input_type=SpecInputType.DFLASH_VERIFY)
         if self.num_tokens_per_req == -1:
             self.num_tokens_per_req = int(self.draft_token_num)
-
-    def get_spec_adjust_token_coefficient(self) -> Tuple[int, int]:
-        return self.draft_token_num, self.draft_token_num
+        self.num_tokens_for_logprob_per_req = int(self.draft_token_num)
 
     def prepare_for_verify(
         self,

python/sglang/srt/speculative/dflash_info_v2.py

@@ -2,7 +2,7 @@
 
 import contextlib
 from dataclasses import dataclass
-from typing import Optional, Tuple
+from typing import Optional
 
 import torch
 
@@ -63,10 +63,9 @@ class DFlashDraftInputV2(SpecInput):
 
     def __post_init__(self):
         super().__init__(spec_input_type=SpecInputType.DFLASH_DRAFT)
-
-    def get_spec_adjust_token_coefficient(self) -> Tuple[int, int]:
         # Spec v2 draft state itself does not change token accounting.
-        return (1, 1)
+        self.num_tokens_per_req = 1
+        self.num_tokens_for_logprob_per_req = 1
 
     def _ensure_prepare_length_buffers(
         self, bs: int, device: torch.device | str

python/sglang/srt/speculative/eagle_draft_cuda_graph_runner.py

@@ -39,6 +39,7 @@ from sglang.srt.runtime_context import get_flags
 from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
 from sglang.srt.speculative.eagle_info import EagleDraftInput
 from sglang.srt.speculative.eagle_utils import get_draft_recurrent_hidden_state_spec
+from sglang.srt.speculative.spec_utils import resolve_num_tokens_per_req
 from sglang.srt.utils import (
     require_attn_tp_gather,
     require_gathered_buffer,
@@ -145,7 +146,10 @@ class EAGLEDraftCudaGraphRunner(DecodeCudaGraphRunner):
 
         # Bucket sizes
         self.capture_bs, _ = get_batch_sizes_to_capture(model_runner)
-        self.num_tokens_per_req = self.topk
+        # Static capture width.
+        self.num_tokens_per_req = resolve_num_tokens_per_req(
+            phase="draft_decode", server_args=model_runner.server_args
+        )
         self.max_bs = max(self.capture_bs)
         self.max_num_token = self.max_bs * self.num_tokens_per_req
 

python/sglang/srt/speculative/eagle_draft_extend_cuda_graph_runner.py

@@ -38,7 +38,10 @@ from sglang.srt.model_executor.runner_backend_utils import (
 from sglang.srt.runtime_context import get_flags
 from sglang.srt.speculative.eagle_info import EagleDraftExtendInput
 from sglang.srt.speculative.eagle_utils import get_draft_input_from_target_hidden_dim
-from sglang.srt.speculative.spec_utils import fast_topk
+from sglang.srt.speculative.spec_utils import (
+    fast_topk,
+    resolve_num_tokens_per_req,
+)
 from sglang.srt.utils import (
     is_hip,
     require_attn_tp_gather,
@@ -131,9 +134,11 @@ class EAGLEDraftExtendCudaGraphRunner(DecodeCudaGraphRunner):
 
         self.capture_bs, _ = get_batch_sizes_to_capture(model_runner)
 
-        # Size cuda-graph buffers by num_draft_tokens (full tree width), not
-        # num_steps + 1, or topk > 1 draft-extend overflows them.
-        self.num_tokens_per_req = model_runner.server_args.speculative_num_draft_tokens
+        # Static capture width: full tree width (num_draft_tokens), not
+        # num_steps + 1 -- topk > 1 draft-extend overflows the buffers.
+        self.num_tokens_per_req = resolve_num_tokens_per_req(
+            phase="draft_extend", server_args=model_runner.server_args
+        )
         self.max_bs = max(self.capture_bs)
         self.max_num_token = self.max_bs * self.num_tokens_per_req
 

python/sglang/srt/speculative/eagle_info.py

@@ -41,6 +41,14 @@ class EagleVerifyInput(SpecInput):
         super().__init__(SpecInputType.EAGLE_VERIFY)
         if self.num_tokens_per_req < 0:
             self.num_tokens_per_req = self.draft_token_num
+        self.num_tokens_for_logprob_per_req = self.draft_token_num
+
+    def get_spec_adjust_token_coefficient(self) -> Tuple[int, int]:
+        # Keep this override on draft_token_num: eagle_worker_v2.verify()
+        # re-stamps num_tokens_per_req = num_steps + 1, which diverges from
+        # the real verify width for topk > 1 trees, and the DP-attention
+        # global-token scaling must follow the actual tree width.
+        return self.draft_token_num, self.draft_token_num
 
     @property
     def max_tree_depth(self) -> int:
@@ -55,9 +63,6 @@ class EagleVerifyInput(SpecInput):
         irregular tree (no fixed per-level branching)."""
         return self.topk
 
-    def get_spec_adjust_token_coefficient(self) -> Tuple[int, int]:
-        return self.draft_token_num, self.draft_token_num
-
     @classmethod
     def create_idle_input(
         cls, topk: int, spec_steps: int, num_verify_tokens: int, device: str
@@ -183,9 +188,6 @@ class EagleDraftInput(SpecInput):
     def __post_init__(self):
         super().__init__(SpecInputType.EAGLE_DRAFT)
 
-    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,
@@ -345,9 +347,6 @@ class EagleDraftExtendInput(SpecInput):
     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,

python/sglang/srt/speculative/eagle_worker_v2.py

@@ -1510,6 +1510,9 @@ class EAGLEWorkerV2(BaseSpecWorker):
         verify_input: EagleVerifyInput = batch.spec_info
         record_stream_for_v2_verify(batch, verify_input, fwd_stream)
 
+        # Actual-width stamp; equals the real width (draft_token_num) only on
+        # the topk-1 chain. Tree consumers must read draft_token_num -- see
+        # EagleVerifyInput.get_spec_adjust_token_coefficient.
         verify_input.num_tokens_per_req = self.speculative_num_steps + 1
         bs = len(batch.seq_lens)
 

python/sglang/srt/speculative/frozen_kv_mtp_cuda_graph_runner.py

@@ -34,6 +34,7 @@ from sglang.srt.model_executor.runner_backend_utils import (
 )
 from sglang.srt.runtime_context import get_flags
 from sglang.srt.speculative.frozen_kv_mtp_info import FrozenKVMTPDraftInput
+from sglang.srt.speculative.spec_utils import resolve_num_tokens_per_req
 from sglang.srt.utils import (
     require_attn_tp_gather,
     require_gathered_buffer,
@@ -113,7 +114,10 @@ class FrozenKVMTPCudaGraphRunner(DecodeCudaGraphRunner):
         self.capture_forward_mode = ForwardMode.DECODE
         self.capture_hidden_mode = CaptureHiddenMode.LAST
 
-        self.num_tokens_per_req = self.topk
+        # Static capture width.
+        self.num_tokens_per_req = resolve_num_tokens_per_req(
+            phase="draft_decode", server_args=model_runner.server_args
+        )
         self.capture_bs, _ = get_batch_sizes_to_capture(
             model_runner, self.num_tokens_per_req
         )
@@ -269,6 +273,7 @@ class FrozenKVMTPCudaGraphRunner(DecodeCudaGraphRunner):
             bonus_tokens=bonus_tokens,
             capture_hidden_mode=CaptureHiddenMode.LAST,
         )
+        # Actual width of the next draft-decode forward: topk tokens per req.
         spec_info.num_tokens_per_req = self.topk
         spec_info.num_tokens_for_logprob_per_req = self.topk
         spec_info.positions = positions

python/sglang/srt/speculative/frozen_kv_mtp_worker_v2.py

@@ -420,6 +420,7 @@ class FrozenKVMTPDraftWorker(EagleDraftWorkerBase, TpModelWorker):
         # gates SWA eviction timing and the SWA prefix-lock release.
 
         spec_info.capture_hidden_mode = CaptureHiddenMode.LAST
+        # Actual width of the next draft-decode forward: topk tokens per req.
         spec_info.num_tokens_per_req = self.topk
         spec_info.num_tokens_for_logprob_per_req = self.topk
         spec_info.positions = self._position_for_batch(batch)

python/sglang/srt/speculative/multi_layer_eagle_draft_extend_cuda_graph_runner.py

@@ -62,7 +62,10 @@ from sglang.srt.model_executor.runner_backend_utils import (
 from sglang.srt.runtime_context import get_flags
 from sglang.srt.speculative.eagle_info import EagleDraftExtendInput
 from sglang.srt.speculative.eagle_utils import get_draft_input_from_target_hidden_dim
-from sglang.srt.speculative.spec_utils import fast_topk
+from sglang.srt.speculative.spec_utils import (
+    fast_topk,
+    resolve_num_tokens_per_req,
+)
 from sglang.srt.utils import (
     get_available_gpu_memory,
     require_attn_tp_gather,
@@ -159,7 +162,9 @@ class MultiLayerEagleDraftExtendCudaGraphRunner(DecodeCudaGraphRunner):
 
         # Fixed window: every step extends each request by the same number of
         # tokens, which lets all steps share one buffer set.
-        self.num_tokens_per_req = self.speculative_num_draft_tokens
+        self.num_tokens_per_req = resolve_num_tokens_per_req(
+            phase="draft_extend", server_args=model_runner.server_args
+        )
         self.max_bs = max(self.capture_bs)
         self.max_num_token = self.max_bs * self.num_tokens_per_req
         self.extend_seq_lens_cpu = [self.num_tokens_per_req] * self.max_bs
@@ -681,6 +686,7 @@ class MultiLayerEagleMultiStepDraftExtendCudaGraphRunner:
             num_correct_drafts=buffers.num_correct_drafts[:bs],
             num_accept_tokens=buffers.num_accept_tokens[:bs],
         )
+        # Actual width of the captured forward == static width by construction.
         spec_info.num_tokens_per_req = self.num_tokens_per_req
         spec_info.num_tokens_for_logprob_per_req = 1
         spec_info.positions = buffers.positions[:padded_num_tokens]

python/sglang/srt/speculative/multi_layer_eagle_worker_v2.py

@@ -515,6 +515,7 @@ class MultiLayerEagleDraftWorker(EagleDraftWorkerBase):
         # Batch 2: Draft extend
         draft_extend_input = EagleDraftExtendInput(
             hidden_states=batch_result.logits_output.hidden_states,
+            # Actual width: the multi-layer chain fills num_steps + 1 rows/req.
             num_tokens_per_req=self.speculative_num_steps + 1,
             num_tokens_for_logprob_per_req=1,
         )

python/sglang/srt/speculative/ngram_info.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Optional, Tuple
+from typing import Optional
 
 import torch
 
@@ -33,6 +33,8 @@ class NgramVerifyInput(SpecInput):
         self.retrieve_next_token = retrieve_next_token
         self.retrieve_next_sibling = retrieve_next_sibling
         self.draft_token_num = draft_token_num
+        self.num_tokens_per_req = draft_token_num
+        self.num_tokens_for_logprob_per_req = draft_token_num
         self.grammar = grammar
 
         # Inputs for V2 overlap worker
@@ -57,9 +59,6 @@ class NgramVerifyInput(SpecInput):
         # Irregular tree: per-level branching follows the corpus matches.
         return -1
 
-    def get_spec_adjust_token_coefficient(self) -> Tuple[int, int]:
-        return self.draft_token_num, self.draft_token_num
-
     def generate_attn_arg_prefill(
         self,
         req_pool_indices: torch.Tensor,

python/sglang/srt/speculative/spec_info.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import warnings
-from abc import ABC, abstractmethod
+from abc import ABC
 from enum import Enum, IntEnum, auto
 from typing import TYPE_CHECKING, Callable, List, Optional, Tuple, Type, Union
 
@@ -314,6 +314,12 @@ class SpecInput(ABC):
     # assignment, so an init-time default would clobber the passed layout.
     ragged_verify_layout: Optional[RaggedVerifyLayout] = None
 
+    # Uniform per-request token width of this forward (and its logits-row
+    # counterpart). Doubles as the DP-attention global_num_tokens multiplier
+    # (ragged forwards carry 1 there). -1 = not set by this flow.
+    num_tokens_per_req: int = -1
+    num_tokens_for_logprob_per_req: int = -1
+
     # DSA MTP IndexShare seed relay. Class-level defaults (same rationale as
     # ragged_verify_layout) so scheduler/relay/attention code reads them
     # uniformly on any SpecInput; only the EAGLE-family inputs override them.
@@ -344,9 +350,8 @@ class SpecInput(ABC):
             SpecInputType.NGRAM_VERIFY,
         }
 
-    @abstractmethod
     def get_spec_adjust_token_coefficient(self) -> Tuple[int, int]:
-        pass
+        return self.num_tokens_per_req, self.num_tokens_for_logprob_per_req
 
     def get_spec_adjusted_global_num_tokens(
         self, batch: ScheduleBatch

python/sglang/srt/speculative/spec_utils.py

@@ -5,7 +5,7 @@ import logging
 import os
 import time
 from contextlib import contextmanager
-from typing import TYPE_CHECKING, Any, List, Optional, Tuple
+from typing import TYPE_CHECKING, Any, List, Literal, Optional, Tuple
 
 import torch
 from huggingface_hub import snapshot_download
@@ -87,6 +87,32 @@ if _is_cpu:
 logger = logging.getLogger(__name__)
 
 
+def resolve_num_tokens_per_req(
+    *,
+    phase: Literal["draft_decode", "draft_extend", "target_verify"],
+    server_args: ServerArgs,
+    spec_algorithm=None,
+    is_draft_worker: bool = False,
+    num_draft_tokens: Optional[int] = None,
+) -> int:
+    """Single static derivation point for a spec phase's per-request token
+    width (sizes capture shapes / buffers); the per-forward dynamic width
+    lives on ``SpecInput.num_tokens_per_req``. Draft phases are
+    EAGLE-family-only; "target_verify" is algorithm-generic via the hook.
+    """
+    if phase == "draft_decode":
+        return server_args.speculative_eagle_topk
+    if phase == "draft_extend":
+        return server_args.speculative_num_draft_tokens
+    if phase == "target_verify":
+        if num_draft_tokens is None:
+            num_draft_tokens = server_args.speculative_num_draft_tokens
+        return spec_algorithm.get_num_tokens_per_req_for_target_verify(
+            num_draft_tokens, is_draft_worker
+        )
+    raise ValueError(f"Unknown speculative phase: {phase}")
+
+
 def fast_sample(probs: torch.Tensor, num_samples: int = 1):
     sample_index = torch.multinomial(probs, num_samples=num_samples)
     sample_p = probs.gather(1, sample_index)