[Spec] Converge DP-attention spec width scaling onto num_tokens_per_req (#31244)

python/sglang/srt/model_executor/forward_batch_info.py

@@ -760,9 +760,16 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
 
             # process global_num_tokens and global_num_tokens_for_logprob
             if batch.spec_info is not None:
-                spec_info: SpecInput = batch.spec_info
+                from sglang.srt.speculative.spec_info import (
+                    spec_scale_global_num_tokens,
+                )
+
                 global_num_tokens, global_num_tokens_for_logprob = (
-                    spec_info.get_spec_adjusted_global_num_tokens(batch)
+                    spec_scale_global_num_tokens(
+                        batch.spec_info,
+                        batch.global_num_tokens,
+                        batch.global_num_tokens_for_logprob,
+                    )
                 )
             else:
                 global_num_tokens = batch.global_num_tokens
@@ -1233,6 +1240,7 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
                 if self.forward_mode.is_idle():
                     self._original_forward_mode = self.forward_mode
                     self.forward_mode = ForwardMode.TARGET_VERIFY
+                # Invert the spec_scale_global_num_tokens scaling.
                 bs = self.batch_size = num_tokens // self.spec_info.num_tokens_per_req
             elif self.is_extend_in_batch and dp_padding_mode.is_max_len():
                 self._original_forward_mode = self.forward_mode
@@ -1294,6 +1302,7 @@ class ForwardBatch(ForwardBatchDeepSeekMHAMixin):
                     self.extend_logprob_start_lens_cpu = self.extend_prefix_lens_cpu
             else:
                 if self.spec_info is not None:
+                    # Invert the spec_scale_global_num_tokens scaling.
                     bs = self.batch_size = (
                         num_tokens // self.spec_info.num_tokens_per_req
                     )

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

@@ -511,13 +511,9 @@ class DecodeCudaGraphRunner(BaseCudaGraphRunner):
             return False
 
         if self.require_mlp_tp_gather:
-            cuda_graph_bs = (
-                max(forward_batch.global_num_tokens_cpu) // self.num_tokens_per_req
-                if self.model_runner.spec_algorithm.is_eagle()
-                or self.model_runner.spec_algorithm.is_standalone()
-                or self.model_runner.spec_algorithm.is_dflash_family()
-                else max(forward_batch.global_num_tokens_cpu)
-            )
+            # Raw sync values are per-rank request counts on decode-family
+            # rounds -- no width division, no per-algorithm enumeration.
+            cuda_graph_bs = max(forward_batch.original_global_num_tokens_cpu)
         else:
             cuda_graph_bs = forward_batch.batch_size
 

python/sglang/srt/models/mindspore.py

@@ -267,13 +267,13 @@ class MindSporeForCausalLM(torch.nn.Module):
         is_prefill = self._is_prefill(forward_batch)
         batch_valid_length = forward_batch.seq_lens.cpu().numpy()
         if forward_batch.forward_mode.is_target_verify():
-            batch_valid_length += forward_batch.spec_info.num_tokens_per_req
+            batch_valid_length += forward_batch.spec_info.draft_token_num
         if forward_batch.extend_seq_lens is not None:
             q_seq_lens = forward_batch.extend_seq_lens.cpu().numpy()
         else:
             q_seq_lens = np.ones([forward_batch.batch_size], dtype=np.int32)
             if forward_batch.forward_mode.is_target_verify():
-                q_seq_lens = q_seq_lens * forward_batch.spec_info.num_tokens_per_req
+                q_seq_lens = q_seq_lens * forward_batch.spec_info.draft_token_num
 
         page_size = get_token_to_kv_pool().page_size
         block_tables = tensor_torch2ms(

python/sglang/srt/speculative/dspark_components/dspark_draft.py

@@ -21,7 +21,10 @@ from sglang.srt.speculative.dspark_components.dspark_planner import VerifyWindow
 from sglang.srt.speculative.dspark_components.kernels.dspark_draft_model import (
     SampleStepTokens,
 )
-from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
+from sglang.srt.speculative.spec_info import (
+    SpeculativeAlgorithm,
+    spec_scale_global_num_tokens,
+)
 from sglang.srt.speculative.spec_utils import draft_tp_context
 
 logger = logging.getLogger(__name__)
@@ -406,8 +409,10 @@ class DraftBlockProposer:
     ) -> None:
         if not self._dp_moe_sync or batch.global_num_tokens is None:
             return
-        gnt, gnt_logprob = (
-            self._draft_block_spec_info.get_spec_adjusted_global_num_tokens(batch)
+        gnt, gnt_logprob = spec_scale_global_num_tokens(
+            self._draft_block_spec_info,
+            batch.global_num_tokens,
+            batch.global_num_tokens_for_logprob,
         )
         device = self.draft_model_runner.device
         forward_batch.global_num_tokens_cpu = gnt

python/sglang/srt/speculative/eagle_draft_cuda_graph_runner.py

@@ -293,12 +293,8 @@ class EAGLEDraftCudaGraphRunner(DecodeCudaGraphRunner):
     # -----------------------------------------------------------------
     def can_run_graph(self, forward_batch: ForwardBatch):
         if self.require_mlp_tp_gather:
-            cuda_graph_bs = (
-                max(forward_batch.global_num_tokens_cpu) // self.num_tokens_per_req
-                if self.model_runner.spec_algorithm.is_eagle()
-                or self.model_runner.spec_algorithm.is_standalone()
-                else max(forward_batch.global_num_tokens_cpu)
-            )
+            # Raw sync values are per-rank request counts on decode-family rounds.
+            cuda_graph_bs = max(forward_batch.original_global_num_tokens_cpu)
         else:
             cuda_graph_bs = forward_batch.batch_size
 

python/sglang/srt/speculative/eagle_draft_extend_cuda_graph_runner.py

@@ -290,12 +290,8 @@ class EAGLEDraftExtendCudaGraphRunner(DecodeCudaGraphRunner):
 
     def can_run_graph(self, forward_batch: ForwardBatch):
         if self.require_mlp_tp_gather:
-            cuda_graph_bs = (
-                max(forward_batch.global_num_tokens_cpu) // self.num_tokens_per_req
-                if self.model_runner.spec_algorithm.is_eagle()
-                or self.model_runner.spec_algorithm.is_standalone()
-                else max(forward_batch.global_num_tokens_cpu)
-            )
+            # Raw sync values are per-rank request counts on decode-family rounds.
+            cuda_graph_bs = max(forward_batch.original_global_num_tokens_cpu)
         else:
             cuda_graph_bs = forward_batch.seq_lens.numel()
 

python/sglang/srt/speculative/eagle_info.py

@@ -1,6 +1,6 @@
 import logging
 from dataclasses import dataclass
-from typing import List, Optional, Tuple
+from typing import List, Optional
 
 import torch
 
@@ -43,13 +43,6 @@ class EagleVerifyInput(SpecInput):
             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:
         """Longest root-to-leaf chain of the verify tree, incl. the root;

python/sglang/srt/speculative/eagle_worker_v2.py

@@ -1472,10 +1472,6 @@ 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)
 
         # Batch 1: Target verify

python/sglang/srt/speculative/frozen_kv_mtp_cuda_graph_runner.py

@@ -202,8 +202,11 @@ class FrozenKVMTPCudaGraphRunner(DecodeCudaGraphRunner):
 
     def can_run_graph(self, forward_batch: ForwardBatch):
         if self.require_mlp_tp_gather:
-            cuda_graph_bs = max(forward_batch.global_num_tokens_cpu) // (
-                self.topk * self.topk
+            # Raw sync values are per-rank request counts on decode-family
+            # rounds; / topk maps the expanded batch to graph-key units
+            # (mirrors the non-gather branch below).
+            cuda_graph_bs = (
+                max(forward_batch.original_global_num_tokens_cpu) // self.topk
             )
         else:
             cuda_graph_bs = (

python/sglang/srt/speculative/frozen_kv_mtp_info.py

@@ -57,4 +57,6 @@ class FrozenKVMTPVerifyInput(EagleVerifyInput):
     """Verify input for Frozen-KV MTP."""
 
     def __post_init__(self):
-        SpecInput.__init__(self, SpecInputType.FROZEN_KV_MTP_VERIFY)
+        # Run EagleVerifyInput's width auto-fill, then correct the type tag.
+        super().__post_init__()
+        self.spec_input_type = SpecInputType.FROZEN_KV_MTP_VERIFY

python/sglang/srt/speculative/multi_layer_eagle_draft_extend_cuda_graph_runner.py

@@ -201,11 +201,8 @@ class MultiLayerEagleDraftExtendCudaGraphRunner(DecodeCudaGraphRunner):
 
     def can_run_graph(self, forward_batch: ForwardBatch):
         if self.require_mlp_tp_gather:
-            cuda_graph_bs = (
-                max(forward_batch.global_num_tokens_cpu) // self.num_tokens_per_req
-                if self.model_runner.spec_algorithm.is_eagle()
-                else max(forward_batch.global_num_tokens_cpu)
-            )
+            # Raw sync values are per-rank request counts on decode-family rounds.
+            cuda_graph_bs = max(forward_batch.original_global_num_tokens_cpu)
         else:
             cuda_graph_bs = forward_batch.seq_lens.numel()
 

python/sglang/srt/speculative/spec_info.py

@@ -350,18 +350,22 @@ class SpecInput(ABC):
             SpecInputType.NGRAM_VERIFY,
         }
 
-    def get_spec_adjust_token_coefficient(self) -> Tuple[int, int]:
-        return self.num_tokens_per_req, self.num_tokens_for_logprob_per_req
 
-    def get_spec_adjusted_global_num_tokens(
-        self, batch: ScheduleBatch
-    ) -> Tuple[List[int], List[int]]:
-        c1, c2 = self.get_spec_adjust_token_coefficient()
-        global_num_tokens = [x * c1 for x in batch.global_num_tokens]
-        global_num_tokens_for_logprob = [
-            x * c2 for x in batch.global_num_tokens_for_logprob
-        ]
-        return global_num_tokens, global_num_tokens_for_logprob
+def spec_scale_global_num_tokens(
+    spec_info: SpecInput,
+    global_num_tokens: List[int],
+    global_num_tokens_for_logprob: List[int],
+) -> Tuple[List[int], List[int]]:
+    """Scale the raw per-rank sync values (request counts on decode-family
+    rounds) into this forward's token units using the spec input's uniform
+    per-request widths."""
+    return (
+        [x * spec_info.num_tokens_per_req for x in global_num_tokens],
+        [
+            x * spec_info.num_tokens_for_logprob_per_req
+            for x in global_num_tokens_for_logprob
+        ],
+    )
 
 
 def create_dummy_verify_input(