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[Elastic EP] Fix recovery lifecycle and add manual coverage (#31744)

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Co-authored-by: Shangming Cai <csmthu@gmail.com>
This commit is contained in:
Xun Sun
2026-07-25 14:32:47 +08:00
committed by GitHub
co-authored by Shangming Cai
parent f9c14e6bd4
commit 9eb2dccbb7
7 changed files with 310 additions and 45 deletions
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python/sglang/srt/elastic_ep/elastic_ep.py
+7 -9
View File
@@ -11,9 +11,10 @@ from sglang.srt.distributed import get_world_group, parallel_state
from sglang.srt.distributed.utils import get_global_tcp_store
from sglang.srt.eplb.expert_location import broadcast_global_expert_location_metadata
from sglang.srt.managers.schedule_batch import ServerArgs
from sglang.srt.utils import broadcast_pyobj, is_cpu, is_cuda
from sglang.srt.utils import is_cpu, is_cuda
if TYPE_CHECKING:
from sglang.srt.configs.model_config import ModelConfig
from sglang.srt.eplb.eplb_manager import EPLBManager
logger = logging.getLogger(__name__)
@@ -462,7 +463,8 @@ def maybe_recover_ep_ranks(
*,
tp_group: parallel_state.GroupCoordinator,
eplb_manager: EPLBManager,
random_seed: int,
model_config: ModelConfig,
moe_ep_rank: int,
) -> bool:
# TODO(perf): `active_ranks.all()` on a CUDA tensor triggers host-device
# synchronization, and this function is on the forward-path.
@@ -489,17 +491,13 @@ def maybe_recover_ep_ranks(
if ranks_to_recover and try_recover_ranks(ranks_to_recover):
eplb_manager.reset_generator()
broadcast_global_expert_location_metadata(
model_config=model_config,
moe_ep_rank=moe_ep_rank,
src_rank=get_healthy_expert_location_src_rank(
invoked_in_elastic_ep_rejoin_path=False
)
),
)
ElasticEPStateManager.instance().reset()
broadcast_pyobj(
[random_seed],
parallel_state.get_world_group().rank,
parallel_state.get_world_group().cpu_group,
src=parallel_state.get_world_group().ranks[0],
)
logger.info(f"recover ranks {ranks_to_recover} done")
return True
python/sglang/srt/managers/scheduler.py
+6
View File
@@ -885,6 +885,12 @@ class Scheduler(
if model_runner.token_to_kv_pool.post_capture_active:
model_runner.post_capture_resize_kv_pool()
if (
self.server_args.elastic_ep_backend is not None
and self.server_args.ep_join_mode == "recover"
):
model_runner.post_capture_elastic_ep_recover()
# Dispatch the model worker
if self.spec_algorithm.is_none():
self.model_worker = self.tp_worker
python/sglang/srt/managers/tp_worker.py
+2 -2
View File
@@ -342,8 +342,8 @@ class TpModelWorker(BaseTpWorker):
self.world_group = get_world_group()
# Sync random seed across TP workers.
# Scale joiners cannot enter the launch-time WORLD broadcast.
if server_args.is_ep_scale_joiner:
# Elastic joiners cannot enter the launch-time WORLD broadcast.
if server_args.is_ep_joiner:
self.random_seed = server_args.random_seed
else:
self.random_seed = broadcast_pyobj(
python/sglang/srt/model_executor/model_runner.py
+36 -30
View File
@@ -399,39 +399,27 @@ class ModelRunner:
def _initialize_elastic_ep_joiner(self) -> None:
if not (
self.server_args.elastic_ep_backend is not None
and self.server_args.is_ep_joiner
and self.server_args.is_ep_scale_joiner
):
return
is_scale_join = self.server_args.ep_join_mode == "scale"
if is_scale_join:
join_effective_ep_size = (
self.server_args.ep_join_rank_offset + self.ps.tp_size
join_effective_ep_size = self.server_args.ep_join_rank_offset + self.ps.tp_size
dist.barrier(group=self.tp_group.cpu_group)
if self.ps.tp_rank == 0:
register_scale_cohort(
self.server_args.ep_join_rank_offset,
join_effective_ep_size,
)
dist.barrier(group=self.tp_group.cpu_group)
if self.ps.tp_rank == 0:
register_scale_cohort(
self.server_args.ep_join_rank_offset,
join_effective_ep_size,
)
join_scale_process_group()
self.server_args.override(
"elastic_ep.scale_join", ep_size=join_effective_ep_size
)
else:
join_process_groups()
join_scale_process_group()
self.server_args.override(
"elastic_ep.scale_join", ep_size=join_effective_ep_size
)
global_ep_rank = self.ps.tp_rank + self.server_args.ep_join_rank_offset
broadcast_global_expert_location_metadata(
model_config=self.model_config,
moe_ep_rank=global_ep_rank,
src_rank=(
0
if is_scale_join
else get_healthy_expert_location_src_rank(
invoked_in_elastic_ep_rejoin_path=True
)
),
src_rank=0,
)
set_global_expert_distribution_recorder(
ExpertDistributionRecorder.init_new(
@@ -441,10 +429,6 @@ class ModelRunner:
)
)
if not is_scale_join:
ElasticEPStateManager.instance().reset()
return
from sglang.srt.layers.dp_attention import (
enable_joiner_all_gather,
update_dp_attention_post_scale,
@@ -839,6 +823,27 @@ class ModelRunner:
resize.capped_max_running_requests
)
def post_capture_elastic_ep_recover(self):
join_process_groups()
global_ep_rank = self.ps.tp_rank + self.server_args.ep_join_rank_offset
broadcast_global_expert_location_metadata(
model_config=self.model_config,
moe_ep_rank=global_ep_rank,
src_rank=get_healthy_expert_location_src_rank(
invoked_in_elastic_ep_rejoin_path=True
),
)
set_global_expert_distribution_recorder(
ExpertDistributionRecorder.init_new(
self.server_args,
get_global_expert_location_metadata(),
rank=global_ep_rank,
)
)
ElasticEPStateManager.instance().reset()
def init_attention_backends(self):
"""Initialize attention backends only (no cuda graph capture)."""
# Must be called BEFORE init_decode_cuda_graph() so CUDA graph capture
@@ -1089,7 +1094,7 @@ class ModelRunner:
dist_barrier_after_load(
elastic_ep_backend=self.server_args.elastic_ep_backend,
tp_rank=self.ps.tp_rank,
is_ep_scale_joiner=self.server_args.is_ep_scale_joiner,
is_ep_joiner=self.server_args.is_ep_joiner,
)
def maybe_init_dwdp(self):
@@ -1822,7 +1827,8 @@ class ModelRunner:
recovered = maybe_recover_ep_ranks(
tp_group=self.tp_group,
eplb_manager=self.eplb_manager,
random_seed=self.server_args.random_seed,
model_config=self.model_config,
moe_ep_rank=self._elastic_global_rank(),
)
if recovered:
self.forward_pass_id = 0
python/sglang/srt/model_executor/model_runner_components/load_model_utils.py
+2 -2
View File
@@ -301,11 +301,11 @@ def dist_barrier_after_load(
*,
elastic_ep_backend: Optional[str],
tp_rank: int,
is_ep_scale_joiner: bool = False,
is_ep_joiner: bool = False,
) -> None:
if elastic_ep_backend == "mooncake":
# Mooncake does not support `monitored_barrier`
if not is_ep_scale_joiner:
if not is_ep_joiner:
dist.barrier(group=get_tp_group().cpu_group)
else:
# Handle the case where some ranks do not finish loading.
python/sglang/srt/server_args.py
+2 -2
View File
@@ -8984,7 +8984,7 @@ class PortArgs:
# (no availability-based search). If incrementing would
# overflow the valid TCP range, decrement instead.
NUM_DERIVED_PORTS = 5
if server_args.is_ep_scale_joiner:
if server_args.is_ep_joiner:
port_base = server_args.port + ZMQ_TCP_PORT_DELTA
if port_base + NUM_DERIVED_PORTS > 65535:
port_base = server_args.port - ZMQ_TCP_PORT_DELTA
@@ -9004,7 +9004,7 @@ class PortArgs:
assert worker_ports is not None
scheduler_input_port = worker_ports[dp_rank]
is_joiner = server_args.is_ep_scale_joiner
is_joiner = server_args.is_ep_joiner
# Under SGLANG_DISTRIBUTED_INIT_METHOD_OVERRIDE, SGLang never binds
# dist_init_port / nccl_port (rendezvous uses the externally-managed
# store; see distributed/bootstrap.py:_resolve_dist_init_method), so
test/manual/ep/test_elastic_recover.py
+255
View File
@@ -0,0 +1,255 @@
"""Manual single-host Elastic EP recovery test.
Run:
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m pytest \
test/manual/ep/test_elastic_recover.py -v -s
"""
import os
import shlex
import subprocess
import time
import unittest
from pathlib import Path
import requests
from sglang.srt.utils import kill_process_tree
from sglang.test.server_fixtures.disaggregation_fixture import get_rdma_devices_args
from sglang.test.test_utils import (
DEFAULT_MODEL_NAME_FOR_TEST_MLA,
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
CustomTestCase,
try_cached_model,
)
from sglang.utils import wait_for_http_ready
TEST_MODEL = os.environ.get(
"SGLANG_ELASTIC_RECOVER_TEST_MODEL",
try_cached_model(DEFAULT_MODEL_NAME_FOR_TEST_MLA),
)
EP_SIZE = 8
LOCAL_EP_SIZE = 4
DIST_INIT_ADDR = os.environ.get("SGLANG_ELASTIC_RECOVER_DIST_INIT", "127.0.0.1:25555")
PRIMARY_PORT = int(os.environ.get("SGLANG_ELASTIC_RECOVER_PRIMARY_PORT", "21000"))
JOINER_PORT = int(os.environ.get("SGLANG_ELASTIC_RECOVER_JOINER_PORT", "22000"))
RECOVER_WAIT_SECONDS = float(os.environ.get("SGLANG_ELASTIC_RECOVER_WAIT_SECONDS", "5"))
RECOVER_TIMEOUT_SECONDS = float(
os.environ.get("SGLANG_ELASTIC_RECOVER_TIMEOUT_SECONDS", "300")
)
RANDOM_SEED = int(os.environ.get("SGLANG_ELASTIC_RECOVER_RANDOM_SEED", "42"))
ib_devices = get_rdma_devices_args()
def _visible_device_ids() -> list[str]:
visible = os.environ.get("CUDA_VISIBLE_DEVICES")
if visible:
return [device.strip() for device in visible.split(",") if device.strip()]
try:
import torch
return [str(index) for index in range(torch.cuda.device_count())]
except Exception:
return []
def _server_args(node_rank: int, port: int, recover: bool = False) -> list[str]:
args = [
"sglang",
"serve",
"--model-path",
TEST_MODEL,
"--host",
"127.0.0.1",
"--port",
str(port),
"--device",
"cuda",
"--trust-remote-code",
"--tp",
str(EP_SIZE),
"--dp",
str(EP_SIZE),
"--nnodes",
"2",
"--node-rank",
str(node_rank),
"--dist-init-addr",
DIST_INIT_ADDR,
"--random-seed",
str(RANDOM_SEED),
"--enable-dp-attention",
"--enable-dp-lm-head",
"--elastic-ep-backend",
"mooncake",
"--mooncake-ib-device",
ib_devices,
"--moe-a2a-backend",
"mooncake",
"--deepep-mode",
"low_latency",
"--moe-dense-tp-size",
"1",
"--disable-custom-all-reduce",
"--enable-eplb",
"--ep-num-redundant-experts",
"72",
"--chunked-prefill-size",
"512",
"--cuda-graph-max-bs-decode",
"16",
"--mem-fraction-static",
"0.5",
]
if recover:
args.extend(["--elastic-ep-join-mode", "recover"])
extra_args = os.environ.get("SGLANG_ELASTIC_RECOVER_EXTRA_SERVER_ARGS", "")
return args + shlex.split(extra_args)
@unittest.skipUnless(
len(_visible_device_ids()) >= EP_SIZE,
"Elastic EP recovery E2E needs 8 visible GPUs.",
)
class TestElasticRecover4To4(CustomTestCase):
"""Kill one four-rank node and recover it with a fresh process group."""
@classmethod
def setUpClass(cls):
cls.base_url = f"http://127.0.0.1:{PRIMARY_PORT}"
cls.processes: list[subprocess.Popen] = []
cls.log_files = []
cls.log_paths: dict[str, Path] = {}
visible_devices = _visible_device_ids()
cls.primary = cls._launch(
node_rank=0,
port=PRIMARY_PORT,
visible_devices=visible_devices[:LOCAL_EP_SIZE],
name="primary",
)
cls.initial_joiner = cls._launch(
node_rank=1,
port=JOINER_PORT,
visible_devices=visible_devices[LOCAL_EP_SIZE:EP_SIZE],
name="initial_joiner",
)
wait_for_http_ready(
f"{cls.base_url}/health_generate",
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
process=cls.primary,
)
@classmethod
def _launch(
cls,
*,
node_rank: int,
port: int,
visible_devices: list[str],
name: str,
recover: bool = False,
) -> subprocess.Popen:
log_path = Path(f"/tmp/elastic_ep_recover_{name}_{int(time.time())}.log")
log_file = open(log_path, "w")
env = os.environ.copy()
env["CUDA_VISIBLE_DEVICES"] = ",".join(visible_devices)
process = subprocess.Popen(
_server_args(node_rank, port, recover),
env=env,
stdout=log_file,
stderr=subprocess.STDOUT,
)
cls.processes.append(process)
cls.log_files.append(log_file)
cls.log_paths[name] = log_path
print(f"Started {name}; log: {log_path}")
return process
@classmethod
def tearDownClass(cls):
for process in reversed(getattr(cls, "processes", [])):
if process.poll() is None:
kill_process_tree(process.pid, wait_timeout=60)
for log_file in getattr(cls, "log_files", []):
log_file.close()
def _generate(self, routed_dp_rank: int | None = None) -> requests.Response:
payload = {
"text": "The capital of France is",
"sampling_params": {"max_new_tokens": 4, "temperature": 0.0},
}
if routed_dp_rank is not None:
payload["routed_dp_rank"] = routed_dp_rank
return requests.post(f"{self.base_url}/generate", json=payload, timeout=90)
def _generate_ok(self, description: str, routed_dp_rank: int | None = None) -> None:
response = self._generate(routed_dp_rank)
self.assertEqual(response.status_code, 200, f"{description}: {response.text}")
payload = response.json()
generated_text = payload.get("text", "")
self.assertIn(
"paris",
generated_text.casefold(),
f"{description}: unexpected generation: {generated_text!r}",
)
def _wait_for_recover_capture(self) -> None:
deadline = time.monotonic() + RECOVER_TIMEOUT_SECONDS
log_path = self.log_paths["recover_joiner"]
marker = "Capture target decode CUDA graph end"
while time.monotonic() < deadline:
self.assertIsNone(
self.recover_joiner.poll(),
"Recover joiner exited during CUDA graph capture",
)
if log_path.exists() and log_path.read_text(errors="replace").count(
marker
) >= (EP_SIZE - LOCAL_EP_SIZE):
return
time.sleep(2)
self.fail(f"Timed out waiting for recover CUDA graph capture: {log_path}")
def _wait_for_recovered_ranks(self) -> None:
self._wait_for_recover_capture()
self._generate_ok("recovery trigger")
deadline = time.monotonic() + RECOVER_TIMEOUT_SECONDS
marker = f"recover ranks {list(range(LOCAL_EP_SIZE, EP_SIZE))} done"
primary_log = self.log_paths["primary"]
while time.monotonic() < deadline:
self.assertIsNone(
self.recover_joiner.poll(), "Recover joiner exited before rejoining"
)
if (
primary_log.exists()
and primary_log.read_text(errors="replace").count(marker)
>= LOCAL_EP_SIZE
):
for request_index in range(3):
self._generate_ok(f"post-recovery request {request_index + 1}")
return
time.sleep(2)
self.fail(f"Timed out waiting for recovery collective: {primary_log}")
def test_recover_four_ranks(self):
self._generate_ok("initial service")
kill_process_tree(self.initial_joiner.pid, wait_timeout=60)
# Give the terminated schedulers time to disappear before fault handling.
time.sleep(RECOVER_WAIT_SECONDS)
self._generate_ok("degraded service after node1 failure")
visible_devices = _visible_device_ids()
self.recover_joiner = self._launch(
node_rank=1,
port=JOINER_PORT,
visible_devices=visible_devices[LOCAL_EP_SIZE:EP_SIZE],
name="recover_joiner",
recover=True,
)
self._wait_for_recovered_ranks()
if __name__ == "__main__":
unittest.main()