Bringing the parallel runtime up becomes a phase, not a side effect (#40345)

test/manual/test_forward_split_prefill.py

@@ -15,6 +15,7 @@ import torch
 
 from sglang.benchmark.one_batch import TreeCacheNamespace
 from sglang.srt.configs.model_config import ModelConfig
+from sglang.srt.distributed import bootstrap
 from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
 from sglang.srt.model_executor.forward_context import (
@@ -22,7 +23,7 @@ from sglang.srt.model_executor.forward_context import (
     set_forward_context,
 )
 from sglang.srt.model_executor.model_runner import ModelRunner
-from sglang.srt.runtime_context import publish
+from sglang.srt.runtime_context import SpawnRanks, publish
 from sglang.srt.sampling.sampling_params import SamplingParams
 from sglang.srt.server_args import PortArgs, ServerArgs
 from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
@@ -56,10 +57,20 @@ class TestForwardSplitPrefill(CustomTestCase):
 
         cls.port_args = PortArgs.init_new(cls.server_args)
 
-        publish(cls.server_args, role="scheduler")
+        publish(
+            cls.server_args,
+            role="scheduler",
+            ranks=SpawnRanks(world_rank=0, gpu_id=0),
+        )
 
         # Load model and tokenizer
         cls.model_config = ModelConfig.from_server_args(cls.server_args)
+        bootstrap.init_parallel_runtime(
+            server_args=cls.server_args,
+            model_config=cls.model_config,
+            device=cls.device,
+            dist_port=cls.port_args.nccl_port,
+        )
         cls.model_runner = ModelRunner(
             model_config=cls.model_config,
             mem_fraction_static=cls.server_args.mem_fraction_static,