Fix several minor issues in PD disaggregation

python/sglang/srt/disaggregation/decode.py

@@ -419,6 +419,38 @@ def process_prebuilt_extend(
 
 class SchedulerDisaggregationDecodeMixin:
 
+    @torch.no_grad()
+    def event_loop_normal_disagg_decode(self):
+        """A normal scheduler loop for decode worker in disaggregation mode."""
+
+        while True:
+            recv_reqs = self.recv_requests()
+            self.process_input_requests(recv_reqs)
+            # polling and allocating kv cache
+            self.process_decode_queue()
+            batch = self.get_next_disagg_decode_batch_to_run()
+            self.cur_batch = batch
+
+            if batch:
+                # Generate fake extend output.
+                if batch.forward_mode.is_extend():
+                    # Note: Logprobs should be handled on the prefill engine.
+                    self.stream_output(batch.reqs, False)
+                else:
+                    result = self.run_batch(batch)
+                    self.process_batch_result(batch, result)
+
+            if batch is None and (
+                len(self.disagg_decode_transfer_queue.queue)
+                + len(self.disagg_decode_prealloc_queue.queue)
+                == 0
+            ):
+                # When the server is idle, do self-check and re-init some states
+                self.check_memory()
+                self.new_token_ratio = self.init_new_token_ratio
+
+            self.last_batch = batch
+
     def get_next_disagg_decode_batch_to_run(
         self: Scheduler,
     ) -> Optional[Tuple[ScheduleBatch, bool]]:

python/sglang/srt/disaggregation/prefill.py

@@ -171,6 +171,36 @@ class SchedulerDisaggregationPrefillMixin:
     Mixin for Scheduler to handle disaggregation prefill
     """
 
+    @torch.no_grad()
+    def event_loop_normal_disagg_prefill(self):
+        """A normal scheduler loop for prefill worker in disaggregation mode."""
+
+        while True:
+            recv_reqs = self.recv_requests()
+            self.process_input_requests(recv_reqs)
+            self.waiting_queue.extend(
+                self.disagg_prefill_pending_queue.pop_bootstrapped()
+            )
+            self.process_prefill_chunk()
+            batch = self.get_new_batch_prefill()
+            self.cur_batch = batch
+
+            if batch:
+                result = self.run_batch(batch)
+                self.process_batch_result_disagg_prefill(batch, result)
+
+            if len(self.disagg_prefill_inflight_queue) > 0:
+                self.process_disagg_prefill_inflight_queue()
+
+            if batch is None and len(self.disagg_prefill_inflight_queue) == 0:
+                self.check_memory()
+                self.new_token_ratio = self.init_new_token_ratio
+
+            self.last_batch = batch
+            # HACK (byronhsu): reset the batch_is_full flag because we never enter update_running_batch which resets it
+            # Otherwise, it hangs under high concurrency
+            self.running_batch.batch_is_full = False
+
     def process_batch_result_disagg_prefill(
         self: Scheduler, batch: ScheduleBatch, result: GenerationBatchResult
     ) -> None:
@@ -210,7 +240,7 @@ def process_disagg_prefill_inflight_queue(self: Scheduler) -> None:
 
         polls = poll_and_all_reduce(
             [req.disagg_kv_sender for req in self.disagg_prefill_inflight_queue],
-            self.tp_worker.get_tp_cpu_group(),
+            self.attn_tp_cpu_group,
         )
 
         undone_reqs: List[Req] = []

python/sglang/srt/managers/scheduler.py

@@ -484,7 +484,7 @@ def init_memory_pool_and_cache(self):
                 self.tree_cache = HiRadixCache(
                     req_to_token_pool=self.req_to_token_pool,
                     token_to_kv_pool_allocator=self.token_to_kv_pool_allocator,
-                    tp_cache_group=self.tp_worker.get_tp_cpu_group(),
+                    tp_cache_group=self.tp_cpu_group,
                     page_size=self.page_size,
                     hicache_ratio=server_args.hicache_ratio,
                 )
@@ -553,7 +553,7 @@ def init_disaggregation(self):
 
             # The decode requests polling kv cache
             self.disagg_decode_transfer_queue = DecodeTransferQueue(
-                gloo_group=self.tp_worker.get_attention_tp_cpu_group(),
+                gloo_group=self.attn_tp_cpu_group,
                 req_to_metadata_buffer_idx_allocator=req_to_metadata_buffer_idx_allocator,
                 metadata_buffers=metadata_buffers,
             )
@@ -568,7 +568,7 @@ def init_disaggregation(self):
                 scheduler=self,
                 transfer_queue=self.disagg_decode_transfer_queue,
                 tree_cache=self.tree_cache,
-                gloo_group=self.tp_worker.get_attention_tp_cpu_group(),
+                gloo_group=self.attn_tp_cpu_group,
                 tp_rank=self.tp_rank,
                 tp_size=self.tp_size,
                 bootstrap_port=self.server_args.disaggregation_bootstrap_port,
@@ -597,7 +597,7 @@ def init_disaggregation(self):
                 tp_rank=self.tp_rank,
                 tp_size=self.tp_size,
                 bootstrap_port=self.server_args.disaggregation_bootstrap_port,
-                gloo_group=self.tp_worker.get_attention_tp_cpu_group(),
+                gloo_group=self.attn_tp_cpu_group,
                 transfer_backend=self.transfer_backend,
                 scheduler=self,
             )
@@ -664,70 +664,6 @@ def event_loop_overlap(self):
 
             self.last_batch = batch
 
-    @torch.no_grad()
-    def event_loop_normal_disagg_prefill(self):
-        """A normal scheduler loop for prefill worker in disaggregation mode."""
-
-        while True:
-            recv_reqs = self.recv_requests()
-            self.process_input_requests(recv_reqs)
-            self.waiting_queue.extend(
-                self.disagg_prefill_pending_queue.pop_bootstrapped()
-            )
-            self.process_prefill_chunk()
-            batch = self.get_new_batch_prefill()
-            self.cur_batch = batch
-
-            if batch:
-                result = self.run_batch(batch)
-                self.process_batch_result_disagg_prefill(batch, result)
-
-            if len(self.disagg_prefill_inflight_queue) > 0:
-                self.process_disagg_prefill_inflight_queue()
-
-            if batch is None and len(self.disagg_prefill_inflight_queue) == 0:
-                self.check_memory()
-                self.new_token_ratio = self.init_new_token_ratio
-
-            self.last_batch = batch
-            # HACK (byronhsu): reset the batch_is_full flag because we never enter update_running_batch which resets it
-            # Otherwise, it hangs under high concurrency
-            self.running_batch.batch_is_full = False
-
-    @torch.no_grad()
-    def event_loop_normal_disagg_decode(self):
-        """A normal scheduler loop for decode worker in disaggregation mode."""
-
-        while True:
-            recv_reqs = self.recv_requests()
-            self.process_input_requests(recv_reqs)
-            # polling and allocating kv cache
-            self.process_decode_queue()
-            batch = self.get_next_disagg_decode_batch_to_run()
-            self.cur_batch = batch
-
-            if batch:
-                # Generate fake extend output.
-                if batch.forward_mode.is_extend():
-                    # Note: Logprobs should be handled on the prefill engine.
-                    self.stream_output(
-                        batch.reqs, [False for _ in range(len(batch.reqs))]
-                    )
-                else:
-                    result = self.run_batch(batch)
-                    self.process_batch_result(batch, result)
-
-            if batch is None and (
-                len(self.disagg_decode_transfer_queue.queue)
-                + len(self.disagg_decode_prealloc_queue.queue)
-                == 0
-            ):
-                # When the server is idle, do self-check and re-init some states
-                self.check_memory()
-                self.new_token_ratio = self.init_new_token_ratio
-
-            self.last_batch = batch
-
     def recv_requests(self) -> List[Req]:
         """Receive results at tp_rank = 0 and broadcast it to all other TP ranks."""
         if self.attn_tp_rank == 0: