[Spec] Rename accepted_indices -> accept_indices; drop _token_id suffix per Rule 5 (#25038)

python/sglang/srt/layers/utils/logprob.py

@@ -346,14 +346,14 @@ def add_output_logprobs_for_spec_v1(
 
     top_logprobs_nums = batch.top_logprobs_nums
     token_ids_logprobs = batch.token_ids_logprobs
-    accepted_indices = res.accepted_indices
+    accept_indices = res.accept_indices
-    assert len(accepted_indices) == len(logits_output.next_token_logits)
+    assert len(accept_indices) == len(logits_output.next_token_logits)
 
     temperatures = batch.sampling_info.temperatures
     num_draft_tokens = batch.spec_info.draft_token_num
     # acceptance indices are the indices in a "flattened" batch.
     # dividing it to num_draft_tokens will yield the actual batch index.
-    temperatures = temperatures[accepted_indices // num_draft_tokens]
+    temperatures = temperatures[accept_indices // num_draft_tokens]
     if envs.SGLANG_RETURN_ORIGINAL_LOGPROB.get():
         logprobs = torch.nn.functional.log_softmax(
             logits_output.next_token_logits, dim=-1

python/sglang/srt/managers/scheduler_output_processor_mixin.py

@@ -406,7 +406,7 @@ class SchedulerOutputProcessorMixin:
             dp_cooperation_info=batch.dp_cooperation_info,
         )
 
-    def _resolve_spec_overlap_token_ids(
+    def _resolve_spec_overlap_tokens(
         self: Scheduler, result: GenerationBatchResult, batch: ScheduleBatch
     ) -> List[List[int]]:
         """Resolve the padding next token ids for speculative decoding with overlap."""
@@ -487,7 +487,7 @@ class SchedulerOutputProcessorMixin:
 
         if batch.spec_algorithm.is_none() or batch.is_spec_v2:
             if batch.is_spec_v2:
-                next_token_ids = self._resolve_spec_overlap_token_ids(result, batch)
+                next_token_ids = self._resolve_spec_overlap_tokens(result, batch)
             elif isinstance(next_token_ids, list):
                 pass  # MLX path: already a list[int], skip torch round-trip
             else:

python/sglang/srt/speculative/dflash_worker.py

@@ -738,7 +738,7 @@ class DFlashWorker:
         added_vocab_start = int(shard.added_vocab_start_index)
 
         num_tokens = int(hidden_states.shape[0])
-        out_token_ids = torch.empty(
+        out_tokens = torch.empty(
             (num_tokens,), dtype=torch.long, device=hidden_states.device
         )
 
@@ -753,13 +753,13 @@ class DFlashWorker:
                 hs = _cast_hs(hidden_states[start:end])
                 if num_org > 0:
                     base_logits = torch.matmul(hs, weight[:num_org].T)
-                    out_token_ids[start:end] = (
+                    out_tokens[start:end] = (
                         torch.argmax(base_logits, dim=-1).to(torch.long)
                         + org_vocab_start
                     )
                 else:
-                    out_token_ids[start:end] = 0
+                    out_tokens[start:end] = 0
-            return out_token_ids
+            return out_tokens
 
         for start in range(0, num_tokens, int(chunk_size)):
             end = min(num_tokens, start + int(chunk_size))
@@ -812,7 +812,7 @@ class DFlashWorker:
                 )
 
             if tp_size == 1:
-                out_token_ids[start:end] = global_ids.to(torch.long)
+                out_tokens[start:end] = global_ids.to(torch.long)
                 continue
 
             # Gather per-rank maxima and associated global ids, then select the global max.
@@ -869,9 +869,9 @@ class DFlashWorker:
             rank_index[0].copy_(best_rank)
             selected_ids = self._draft_greedy_selected_ids_buf[:, :chunk_len]
             torch.gather(gathered_ids, 0, rank_index, out=selected_ids)
-            out_token_ids[start:end].copy_(selected_ids.view(-1))
+            out_tokens[start:end].copy_(selected_ids.view(-1))
 
-        return out_token_ids
+        return out_tokens
 
     def _append_target_hidden_to_draft_kv(
         self,

python/sglang/srt/speculative/eagle_info.py

@@ -574,7 +574,7 @@ class EagleVerifyInput(SpecInput, EagleVerifyInputV2Mixin):
                 logits_output=logits_output,
                 accept_tokens=accept_tokens,
                 num_correct_drafts_per_req_cpu=num_correct_drafts_list,
-                accepted_indices=accept_index,
+                accept_indices=accept_index,
             )
         else:
             if page_size == 1 or self.topk == 1:
@@ -651,7 +651,7 @@ class EagleVerifyInput(SpecInput, EagleVerifyInputV2Mixin):
                 logits_output=logits_output,
                 accept_tokens=accept_tokens,
                 num_correct_drafts_per_req_cpu=num_correct_drafts_list,
-                accepted_indices=accept_index,
+                accept_indices=accept_index,
             )
 
 
@@ -972,7 +972,7 @@ class EagleVerifyOutput:
     # Accepted token length per sequence in a batch in CPU (full set).
     num_correct_drafts_per_req_cpu: List[int]
     # Accepted indices from logits_output.next_token_logits
-    accepted_indices: torch.Tensor
+    accept_indices: torch.Tensor
 
     @classmethod
     def create_idle(
@@ -988,7 +988,7 @@ class EagleVerifyOutput:
             logits_output=logits_output,
             accept_tokens=torch.empty(0, dtype=torch.long, device=device),
             num_correct_drafts_per_req_cpu=[],
-            accepted_indices=torch.full(
+            accept_indices=torch.full(
                 (0, spec_steps + 1), -1, dtype=torch.int32, device=device
             ),
         )

python/sglang/srt/speculative/eagle_worker.py

@@ -971,9 +971,9 @@ class EAGLEWorker(TpModelWorker):
         # Post process based on verified outputs.
         # Pick indices that we care (accepted)
         logits_output.next_token_logits = logits_output.next_token_logits[
-            res.accepted_indices
+            res.accept_indices
         ]
-        logits_output.hidden_states = logits_output.hidden_states[res.accepted_indices]
+        logits_output.hidden_states = logits_output.hidden_states[res.accept_indices]
 
         if (
             self.target_worker.model_runner.hybrid_gdn_config is not None
@@ -1033,16 +1033,16 @@ class EAGLEWorker(TpModelWorker):
         )
 
         # If topk > 1, we need to use retrieve_next_token and retrieve_next_sibling to handle the eagle tree custom attention mask
-        # res.accepted_indices.shape[0] > 0 skips DP attn idle batch
+        # res.accept_indices.shape[0] > 0 skips DP attn idle batch
-        if spec_info.topk > 1 and res.accepted_indices.shape[0] > 0:
+        if spec_info.topk > 1 and res.accept_indices.shape[0] > 0:
-            # accepted_indices=[0,2,3,4,5,7,9,10,11], num_accept_tokens=[4, 3, 2], cumulative_num_accept_tokens=[4, 7, 9]
+            # accept_indices=[0,2,3,4,5,7,9,10,11], num_accept_tokens=[4, 3, 2], cumulative_num_accept_tokens=[4, 7, 9]
-            # first_token_indices_per_req=prepend(0, accepted_indices[cumulative_num_accept_tokens[:-1]]) = [0, 5, 10]
+            # first_token_indices_per_req=prepend(0, accept_indices[cumulative_num_accept_tokens[:-1]]) = [0, 5, 10]
-            # last_token_indices_per_req=accepted_indices[cumulative_num_accept_tokens - 1] = [4, 9, 11] (last token ID of each req)
+            # last_token_indices_per_req=accept_indices[cumulative_num_accept_tokens - 1] = [4, 9, 11] (last token ID of each req)
             # last_correct_step_indices = [4,4,1]; those are the per-req spec-decoding step offsets that contain the correct mamba caches
             # equivalent: last_correct_step_indices = last_token_indices_per_req - first_token_indices_per_req;
             # `accepted_indices_offset` equals `first_token_indices_per_req` because the first accepted slot of each req is its "current token" at logical position i * draft_token_num.
             last_correct_step_indices = (
-                res.accepted_indices[cumulative_num_accept_tokens - 1]
+                res.accept_indices[cumulative_num_accept_tokens - 1]
                 - accepted_indices_offset
             )
         else:
@@ -1062,7 +1062,7 @@ class EAGLEWorker(TpModelWorker):
             to_track_ith = torch.clamp(tracking_point - seq_lens_pre_verify - 1, min=0)
             mamba_steps_to_track = torch.where(
                 to_track_mask,
-                res.accepted_indices[to_track_ith + accepted_indices_start]
+                res.accept_indices[to_track_ith + accepted_indices_start]
                 - accepted_indices_offset,
                 -1,
             )

python/sglang/srt/speculative/frozen_kv_mtp_worker.py

@@ -757,9 +757,9 @@ class FrozenKVMTPWorker(TpModelWorker):
         )
 
         logits_output.next_token_logits = logits_output.next_token_logits[
-            res.accepted_indices
+            res.accept_indices
         ]
-        logits_output.hidden_states = logits_output.hidden_states[res.accepted_indices]
+        logits_output.hidden_states = logits_output.hidden_states[res.accept_indices]
 
         if (
             self.target_worker.model_runner.hybrid_gdn_config is not None

python/sglang/srt/speculative/multi_layer_eagle_worker.py

@@ -556,9 +556,9 @@ class MultiLayerEagleWorker(TpModelWorker):
         # Post process based on verified outputs.
         # Pick indices that we care (accepted)
         logits_output.next_token_logits = logits_output.next_token_logits[
-            res.accepted_indices
+            res.accept_indices
         ]
-        logits_output.hidden_states = logits_output.hidden_states[res.accepted_indices]
+        logits_output.hidden_states = logits_output.hidden_states[res.accept_indices]
 
         if self.target_worker.model_runner.hybrid_gdn_config is not None:
             num_accept_tokens = (
@@ -571,11 +571,11 @@ class MultiLayerEagleWorker(TpModelWorker):
             )
 
             # If topk > 1, we need to use retrieve_next_token and retrieve_next_sibling to handle the eagle tree custom attention mask
-            # res.accepted_indices.shape[0] > 0 skips DP attn idle batch
+            # res.accept_indices.shape[0] > 0 skips DP attn idle batch
-            if spec_info.topk > 1 and res.accepted_indices.shape[0] > 0:
+            if spec_info.topk > 1 and res.accept_indices.shape[0] > 0:
-                # accepted_indices=[0,2,3,4,5,7,9,10,11], num_accept_tokens=[4, 3, 2], cumulative_num_accept_tokens=[4, 7, 9]
+                # accept_indices=[0,2,3,4,5,7,9,10,11], num_accept_tokens=[4, 3, 2], cumulative_num_accept_tokens=[4, 7, 9]
-                # first_token_indices_per_req=prepend(0, accepted_indices[cumulative_num_accept_tokens[:-1]]) = [0, 5, 10]
+                # first_token_indices_per_req=prepend(0, accept_indices[cumulative_num_accept_tokens[:-1]]) = [0, 5, 10]
-                # last_token_indices_per_req=accepted_indices[cumulative_num_accept_tokens - 1] = [4, 9, 11] (last token ID of each req)
+                # last_token_indices_per_req=accept_indices[cumulative_num_accept_tokens - 1] = [4, 9, 11] (last token ID of each req)
                 # last_correct_step_indices = [4,4,1]; those are the per-req spec-decoding step offsets that contain the correct mamba caches
                 cumulative_num_accept_tokens = torch.cumsum(num_accept_tokens, dim=0)
                 req_start_positions = torch.cat(
@@ -588,8 +588,8 @@ class MultiLayerEagleWorker(TpModelWorker):
                         cumulative_num_accept_tokens[:-1],
                     ]
                 )
-                first_token_indices_per_req = res.accepted_indices[req_start_positions]
+                first_token_indices_per_req = res.accept_indices[req_start_positions]
-                last_token_indices_per_req = res.accepted_indices[
+                last_token_indices_per_req = res.accept_indices[
                     cumulative_num_accept_tokens - 1
                 ]
                 last_correct_step_indices = (

python/sglang/srt/speculative/ngram_info.py

@@ -157,7 +157,7 @@ class NgramVerifyInput(SpecInput):
         batch: ScheduleBatch,
         logits_output: torch.Tensor,
     ):
-        accept_index_cpu = self.accepted_indices.tolist()
+        accept_index_cpu = self.accept_indices.tolist()
         predict_cpu = self.predict.tolist()
         has_finished = False
         think_end_id = batch.model_config.think_end_id
@@ -176,7 +176,7 @@ class NgramVerifyInput(SpecInput):
                 if req.finished():
                     has_finished = True
                     # set all tokens after finished token to -1 and break
-                    self.accepted_indices[i, j + 1 :] = -1
+                    self.accept_indices[i, j + 1 :] = -1
                     break
                 else:
                     if req.grammar is not None:
@@ -185,7 +185,7 @@ class NgramVerifyInput(SpecInput):
                         except ValueError as e:
                             logger.info(
                                 f"{i=}, {req=}\n"
-                                f"{self.accepted_indices=}\n"
+                                f"{self.accept_indices=}\n"
                                 f"{self.predict=}\n"
                             )
                             raise e
@@ -197,17 +197,17 @@ class NgramVerifyInput(SpecInput):
             req.update_spec_correct_drafts_histogram(num_correct_drafts_this_req)
 
         if has_finished:
-            self.num_correct_drafts = (self.accepted_indices != -1).sum(dim=1) - 1
+            self.num_correct_drafts = (self.accept_indices != -1).sum(dim=1) - 1
-        self.accepted_indices = self.accepted_indices[self.accepted_indices != -1]
+        self.accept_indices = self.accept_indices[self.accept_indices != -1]
 
         logits_output.next_token_logits = logits_output.next_token_logits[
-            self.accepted_indices
+            self.accept_indices
         ]
         if logits_output.hidden_states:
             logits_output.hidden_states = logits_output.hidden_states[
-                self.accepted_indices
+                self.accept_indices
             ]
-        self.accept_tokens = self.predict[self.accepted_indices]
+        self.accept_tokens = self.predict[self.accept_indices]
 
     def _free_cache(
         self,
@@ -220,16 +220,16 @@ class NgramVerifyInput(SpecInput):
         if page_size == 1:
             # TODO: boolean array index leads to a device sync. Remove it.
             evict_mask = torch.full_like(self.draft_token, True, dtype=torch.bool)
-            evict_mask[self.accepted_indices] = False
+            evict_mask[self.accept_indices] = False
             batch.token_to_kv_pool_allocator.free(batch.out_cache_loc[evict_mask])
-            batch.out_cache_loc = batch.out_cache_loc[self.accepted_indices]
+            batch.out_cache_loc = batch.out_cache_loc[self.accept_indices]
         else:
             # Shift the accepted tokens to the beginning.
             # Only evict the last part
             src_cache_loc, tgt_cache_loc, to_free_num_slots = get_src_tgt_cache_loc(
                 batch.seq_lens,
                 batch.out_cache_loc,
-                self.accepted_indices,
+                self.accept_indices,
                 self.num_correct_drafts,
                 self.draft_token_num,
                 page_size,
@@ -297,7 +297,7 @@ class NgramVerifyInput(SpecInput):
         predict_shape = list(logits_output.next_token_logits.shape)[:-1]
         predict_shape[-1] += 1
         self.predict = torch.empty(predict_shape, dtype=torch.int32, device=self.device)
-        self.accepted_indices = torch.full(
+        self.accept_indices = torch.full(
             (bs, self.draft_token_num), -1, dtype=torch.int32, device=self.device
         )
         self.num_correct_drafts = torch.empty(
@@ -306,7 +306,7 @@ class NgramVerifyInput(SpecInput):
 
         verify_tree_greedy(
             predicts=self.predict,  # mutable
-            accept_index=self.accepted_indices,  # mutable
+            accept_index=self.accept_indices,  # mutable
             accept_token_num=self.num_correct_drafts,  # mutable
             candidates=candidates,
             # kwarg LHS retained as `retrive_*` to match sgl_kernel op schema.
@@ -327,7 +327,7 @@ class NgramVerifyInput(SpecInput):
         predict_shape = list(logits_output.next_token_logits.shape)[:-1]
         predict_shape[-1] += 1
         self.predict = torch.empty(predict_shape, dtype=torch.int32, device=self.device)
-        self.accepted_indices = torch.full(
+        self.accept_indices = torch.full(
             (bs, self.draft_token_num), -1, dtype=torch.int32, device=self.device
         )
         self.num_correct_drafts = torch.empty(
@@ -371,7 +371,7 @@ class NgramVerifyInput(SpecInput):
         )
         tree_speculative_sampling_target_only(
             predicts=self.predict,  # mutable
-            accept_index=self.accepted_indices,  # mutable
+            accept_index=self.accept_indices,  # mutable
             accept_token_num=self.num_correct_drafts,  # mutable
             candidates=candidates.to(torch.int64),
             # kwarg LHS retained as `retrive_*` to match sgl_kernel op schema.