Minor code style fix for dllm (#14836)

python/sglang/srt/managers/schedule_batch.py

@@ -710,7 +710,6 @@ class Req:
         self.dimensions = dimensions
 
         # For diffusion LLM
-        self.dllm_ids = []
         self.dllm_block_offset = 0
         self.dllm_config = dllm_config
 
@@ -786,22 +785,19 @@ class Req:
     def is_dllm(self):
         return self.dllm_config is not None
 
+    def _init_fill_ids_for_dllm(self):
+        if not self.fill_ids:
+            self.fill_ids = (
+                self.origin_input_ids
+                + [self.dllm_config.mask_id] * self.dllm_config.block_size
+            )
+        else:
+            self.dllm_block_offset += self.dllm_config.block_size
+            self.fill_ids += [self.dllm_config.mask_id] * self.dllm_config.block_size
+
     def init_next_round_input(self, tree_cache: Optional[BasePrefixCache] = None):
         if self.is_dllm():
-            if not self.fill_ids:
-                self.dllm_ids = (
-                    self.origin_input_ids
-                    + [
-                        self.dllm_config.mask_id,
-                    ]
-                    * self.dllm_config.block_size
-                )
-            else:
-                self.dllm_block_offset += self.dllm_config.block_size
-                self.dllm_ids += [
-                    self.dllm_config.mask_id
-                ] * self.dllm_config.block_size
-            self.fill_ids = self.dllm_ids
+            self._init_fill_ids_for_dllm()
         else:
             self.fill_ids = self.origin_input_ids + self.output_ids
 
@@ -1322,9 +1318,11 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
         ), f"Expected {len(self.out_cache_loc)}, got {self.extend_num_tokens}"
 
     def prepare_for_extend(self):
-        self.forward_mode = (
-            ForwardMode.DLLM_EXTEND if self.is_dllm() else ForwardMode.EXTEND
-        )
+        self.forward_mode = ForwardMode.EXTEND
+
+        if self.is_dllm():
+            # For DLLM, we use a separate forward mode
+            self.forward_mode = ForwardMode.DLLM_EXTEND
 
         # Init tensors
         reqs = self.reqs

python/sglang/srt/managers/tp_worker.py

@@ -239,8 +239,11 @@ class TpModelWorker(BaseTpWorker):
             is_draft_model=is_draft_worker,
         )
 
+        # Init DLLM algorithm
         if server_args.dllm_algorithm is not None:
             self.dllm_algorithm = DllmAlgorithm.from_server_args(server_args)
+        else:
+            self.dllm_algorithm = None
 
         self._model_runner = ModelRunner(
             model_config=self.model_config,
@@ -349,7 +352,19 @@ class TpModelWorker(BaseTpWorker):
         )
 
     def is_dllm(self):
-        return hasattr(self, "dllm_algorithm")
+        return self.dllm_algorithm is not None
+
+    def _forward_batch_generation_dllm(
+        self, forward_batch: ForwardBatch
+    ) -> GenerationBatchResult:
+        logits_output, next_token_ids, can_run_cuda_graph = self.dllm_algorithm.run(
+            self.model_runner, forward_batch
+        )
+        return GenerationBatchResult(
+            logits_output=logits_output,
+            next_token_ids=next_token_ids,
+            can_run_cuda_graph=can_run_cuda_graph,
+        )
 
     def forward_batch_generation(
         self,
@@ -380,14 +395,7 @@ class TpModelWorker(BaseTpWorker):
 
         if self.pp_group.is_last_rank:
             if self.is_dllm():
-                logits_output, next_token_ids, can_run_cuda_graph = (
-                    self.dllm_algorithm.run(self.model_runner, forward_batch)
-                )
-                return GenerationBatchResult(
-                    logits_output=logits_output,
-                    next_token_ids=next_token_ids,
-                    can_run_cuda_graph=can_run_cuda_graph,
-                )
+                return self._forward_batch_generation_dllm(forward_batch)
 
             logits_output, can_run_cuda_graph = self.model_runner.forward(
                 forward_batch,

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -832,16 +832,18 @@ class CudaGraphRunner:
             graph_key = self.bs
         self.graphs[graph_key].replay()
         output = self.output_buffers[graph_key]
+
         if isinstance(output, LogitsProcessorOutput):
+            if self.is_dllm:
+                next_token_logits = None
+                full_logits = output.full_logits[: self.raw_num_token]
+            else:
+                full_logits = None
+                next_token_logits = output.next_token_logits[: self.raw_num_token]
+
             return LogitsProcessorOutput(
-                next_token_logits=(
-                    output.next_token_logits[: self.raw_num_token]
-                    if not self.is_dllm
-                    else None
-                ),
-                full_logits=(
-                    output.full_logits[: self.raw_num_token] if self.is_dllm else None
-                ),
+                next_token_logits=next_token_logits,
+                full_logits=full_logits,
                 hidden_states=(
                     output.hidden_states[: self.raw_num_token]
                     if output.hidden_states is not None