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

test/manual/kv_transfer/test_mooncake_transfer_engine_init.py

@@ -45,7 +45,6 @@ def test_mooncake_te_condition(server_args: ServerArgs) -> bool:
     """
     Test the condition logic for using MooncakeTransferEngine.
     """
-    from sglang.srt.model_executor.model_runner import ModelRunner
 
     dummy_runner = SimpleNamespace(server_args=server_args, gpu_id=0)
     init_called = False
@@ -69,7 +68,11 @@ def test_mooncake_te_condition(server_args: ServerArgs) -> bool:
             return_value="127.0.0.1",
         ),
     ):
-        ModelRunner.init_shared_mooncake_transfer_engine(dummy_runner)
+        from sglang.srt.distributed.device_communicators.mooncake_transfer_engine import (
+            maybe_init_shared_mooncake_transfer_engine,
+        )
+
+        maybe_init_shared_mooncake_transfer_engine(gpu_id=dummy_runner.gpu_id)
 
     return init_called
 

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,

test/manual/test_vlm_accuracy.py

@@ -9,6 +9,7 @@ import torch.nn.functional as F
 from transformers import AutoModel, AutoProcessor, AutoTokenizer
 
 from sglang.srt.configs.model_config import ModelConfig
+from sglang.srt.distributed import bootstrap
 from sglang.srt.entrypoints.openai.protocol import ChatCompletionRequest
 from sglang.srt.managers.mm_utils import embed_mm_inputs, init_mm_embedding_cache
 from sglang.srt.managers.schedule_batch import (
@@ -19,7 +20,7 @@ from sglang.srt.managers.schedule_batch import (
 from sglang.srt.model_executor.model_runner import ModelRunner
 from sglang.srt.multimodal.processors.base_processor import BaseMultimodalProcessor
 from sglang.srt.parser.conversation import generate_chat_conv
-from sglang.srt.runtime_context import publish
+from sglang.srt.runtime_context import SpawnRanks, get_device, publish
 from sglang.srt.server_args import ServerArgs
 from sglang.test.test_utils import download_image_with_retry
 
@@ -145,9 +146,20 @@ class VisionLLMLogitsBase(unittest.IsolatedAsyncioTestCase):
             model_path=self.model_path,
             disable_cuda_graph=True,
         )
-        publish(server_args, role="scheduler")
+        publish(
+            server_args,
+            role="scheduler",
+            ranks=SpawnRanks(world_rank=0, gpu_id=0),
+        )
+        model_config = ModelConfig(self.model_path, model_override_args="{}")
+        bootstrap.init_parallel_runtime(
+            server_args=server_args,
+            model_config=model_config,
+            device=get_device().device,
+            dist_port=12435,
+        )
         self.model_runner = ModelRunner(
-            model_config=ModelConfig(self.model_path, model_override_args="{}"),
+            model_config=model_config,
             mem_fraction_static=0.8,
             gpu_id=0,
             nccl_port=12435,