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[1/2] Add ModelExpress coordination for remote instance weight loading - matching TP (#19920)

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Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Ishan Dhanani <ishan@dhanani.dev>
This commit is contained in:
ishandhanani
2026-03-18 15:38:32 -05:00
committed by GitHub
parent 6cca5b9b97
commit 8f0f36c64b
6 changed files with 320 additions and 11 deletions
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33
docs/advanced_features/rfork.md
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@@ -9,11 +9,12 @@ To learn more details about R-Fork, please check **<a href=https://lmsys.org/blo
| Argument | Usage |
|--------------|--------------------------------------------|
| load-format | set to `remote_instance` to enable R-Fork. |
| remote-instance-weight-loader-backend | `nccl` or `transfer_engine`, default value is `nccl` |
| remote-instance-weight-loader-seed-instance-ip | IP address of the seed instance who will provide the model weight |
| remote-instance-weight-loader-seed-instance-service-port | the port that the seed instance's HTTP server is listening on |
| remote-instance-weight-loader-send-weights-group-ports | the list of available ports on the seed instance that will be used to build NCCL communication groups between seed and client instance. This argument is only needed by `nccl` backend. |
| remote-instance-weight-loader-start-seed-via-transfer-engine | set to start seed service that supports TransferEngine as backend. It is needed for seed instances when using `transfer_engine` as backend. |
| remote-instance-weight-loader-backend | `nccl`, `transfer_engine`, or `modelexpress`. Default is `nccl`. |
| remote-instance-weight-loader-seed-instance-ip | IP address of the seed instance who will provide the model weight. Used by `nccl` and `transfer_engine` backends. |
| remote-instance-weight-loader-seed-instance-service-port | the port that the seed instance's HTTP server is listening on. Used by `nccl` and `transfer_engine` backends. |
| remote-instance-weight-loader-send-weights-group-ports | the list of available ports on the seed instance that will be used to build NCCL communication groups between seed and client instance. Only needed by `nccl` backend. |
| remote-instance-weight-loader-start-seed-via-transfer-engine | set to start seed service that supports TransferEngine as backend. Needed for seed instances when using `transfer_engine` as backend. |
| modelexpress-config | JSON config for `modelexpress` backend. Keys: `"url"` (required, gRPC host:port of ModelExpress server), `"model_name"` (optional, defaults to `--model-path`), `"source"` (optional bool, `true` for seed mode). |
### NCCL as backend
@@ -47,3 +48,25 @@ python -m sglang.launch_server [args] \
--remote-instance-weight-loader-seed-instance-service-port [seed_instance_service_port] \
--remote-instance-weight-loader-backend transfer_engine
```
### ModelExpress as backend
[ModelExpress](https://github.com/ai-dynamo/modelexpress) is a coordination service that manages P2P weight transfer metadata. It removes the need for direct seed IP/port configuration by providing a centralized registry that seeds publish to and clients discover from. Under the hood it uses TransferEngine (Mooncake) for the actual RDMA data transfer.
A running ModelExpress server is required. See the [ModelExpress documentation](https://github.com/ai-dynamo/modelexpress) for setup instructions.
seed instance:
```shell
python -m sglang.launch_server [args] \
--modelexpress-config '{"url": "[modelexpress_grpc_host:port]", "model_name": "[model_name]", "source": true}'
```
client instance:
```shell
python -m sglang.launch_server [args] \
--load-format remote_instance \
--remote-instance-weight-loader-backend modelexpress \
--modelexpress-config '{"url": "[modelexpress_grpc_host:port]", "model_name": "[model_name]"}'
```
The seed publishes its TransferEngine session ID and tensor layout to ModelExpress. The client queries ModelExpress to discover the seed, then pulls weights directly via RDMA. This enables dynamic seed discovery without hardcoding IPs, and supports multiple models through a single ModelExpress instance.

2
python/sglang/srt/configs/load_config.py
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@@ -76,6 +76,8 @@ class LoadConfig:
remote_instance_weight_loader_send_weights_group_ports: Optional[List[int]] = None
remote_instance_weight_loader_backend: Optional[str] = None
remote_instance_weight_loader_transfer_engine: Optional[Any] = None
modelexpress_url: Optional[str] = None
modelexpress_model_name: Optional[str] = None
# ModelOpt-specific loading options
modelopt_checkpoint_restore_path: Optional[str] = None

90
python/sglang/srt/model_executor/model_runner.py
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@@ -676,6 +676,74 @@ class ModelRunner(ModelRunnerKVCacheMixin):
local_ip, self.remote_instance_transfer_engine.get_rpc_port()
).to_host_port_str()
def _publish_modelexpress_metadata(self):
"""Publish TransferEngine metadata to ModelExpress server (seed mode)."""
try:
from modelexpress import p2p_pb2
from modelexpress.client import MxClient
except ImportError as exc:
raise ImportError(
"ModelExpress support requires the 'modelexpress' package. "
"Install it with: pip install modelexpress"
) from exc
model_name = (
self.server_args.modelexpress_model_name or self.server_args.model_path
)
mx_url = self.server_args.modelexpress_url
session_id = self.remote_instance_transfer_engine_session_id
weight_info = self.remote_instance_transfer_engine_weight_info
if not session_id or weight_info is None:
logger.warning(
"ModelExpress source: skipping publish -- "
"TransferEngine not initialized or no weight info"
)
return
# Build tensor descriptors from weight_info dict
tensors = []
for name, (addr, numel, element_size) in weight_info.items():
tensors.append(
p2p_pb2.TensorDescriptor(
name=name,
addr=addr,
size=numel * element_size,
device_id=self.gpu_id,
)
)
worker = p2p_pb2.WorkerMetadata(
worker_rank=self.tp_rank,
transfer_engine_session_id=session_id,
tensors=tensors,
)
mx_client = MxClient(server_url=mx_url)
try:
logger.info(
"ModelExpress source: publishing metadata for model=%s, "
"tp_rank=%d, session=%s, %d tensors",
model_name,
self.tp_rank,
session_id,
len(tensors),
)
mx_client.publish_metadata(model_name, [worker])
mx_client.publish_ready(
model_name,
worker_id=self.tp_rank,
session_id=mx_client.session_id,
metadata_hash="",
)
logger.info(
"ModelExpress source: published ready for model=%s, tp_rank=%d",
model_name,
self.tp_rank,
)
finally:
mx_client.close()
def model_specific_adjustment(self):
server_args = self.server_args
@@ -963,6 +1031,9 @@ class ModelRunner(ModelRunnerKVCacheMixin):
remote_instance_weight_loader_send_weights_group_ports=self.server_args.remote_instance_weight_loader_send_weights_group_ports,
remote_instance_weight_loader_backend=self.server_args.remote_instance_weight_loader_backend,
remote_instance_weight_loader_transfer_engine=self.remote_instance_transfer_engine,
modelexpress_url=self.server_args.modelexpress_url,
modelexpress_model_name=self.server_args.modelexpress_model_name
or self.server_args.model_path,
modelopt_config=modelopt_config,
rl_quant_profile=self.server_args.rl_quant_profile,
draft_model_idx=self.draft_model_idx,
@@ -1015,6 +1086,25 @@ class ModelRunner(ModelRunnerKVCacheMixin):
)
monkey_patch_vllm_parallel_state(reverse=True)
# Publish metadata to ModelExpress if running as seed source
if self.server_args.modelexpress_source:
# Seed loads via DefaultModelLoader (load_format=auto), which doesn't
# call register_memory_region(). Do it here so weight_info is populated.
if (
self.remote_instance_transfer_engine_weight_info is None
and self.remote_instance_transfer_engine is not None
):
from sglang.srt.model_loader.remote_instance_weight_loader_utils import (
register_memory_region,
)
self.remote_instance_transfer_engine_weight_info = (
register_memory_region(
self.model, self.remote_instance_transfer_engine
)
)
self._publish_modelexpress_metadata()
get_offloader().post_init()
# Register model for layerwise NVTX profiling if enabled

138
python/sglang/srt/model_loader/loader.py
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@@ -2151,6 +2151,15 @@ class RemoteInstanceModelLoader(BaseModelLoader):
raise RuntimeError(
"Failed to load weights from remote instance via transfer engine."
)
elif (
load_config.remote_instance_weight_loader_backend
== RemoteInstanceWeightLoaderBackend.MODELEXPRESS
):
self.load_model_from_modelexpress(
model,
load_config,
device_config,
)
else:
raise ValueError("Invalid remote instance weight loader backend.")
@@ -2264,6 +2273,135 @@ class RemoteInstanceModelLoader(BaseModelLoader):
return True
def load_model_from_modelexpress(
self,
model,
load_config: LoadConfig,
device_config: DeviceConfig,
):
"""Load weights via ModelExpress coordination + TransferEngine RDMA."""
try:
from modelexpress.client import MxClient
except ImportError as exc:
raise ImportError(
"ModelExpress support requires the 'modelexpress' package. "
"Install it with: pip install modelexpress"
) from exc
transfer_engine = load_config.remote_instance_weight_loader_transfer_engine
if transfer_engine is None:
raise RuntimeError(
"TransferEngine is not initialized for modelexpress backend."
)
tp_rank = load_config.tp_rank
model_name = load_config.modelexpress_model_name
logger.info(
"ModelExpress: registering memory regions for tp_rank=%d...", tp_rank
)
self.remote_instance_transfer_engine_weight_info = register_memory_region(
model, transfer_engine
)
# Wait for seed to be ready via ModelExpress
mx_client = MxClient(server_url=load_config.modelexpress_url)
try:
logger.info(
"ModelExpress: waiting for seed ready (model=%s)...",
model_name,
)
ready, session_id, metadata_hash = mx_client.wait_for_ready(
model_name,
worker_id=tp_rank,
)
if not ready:
raise RuntimeError(
f"ModelExpress: timed out waiting for seed ready "
f"(model={model_name}, worker={tp_rank})"
)
response = mx_client.get_metadata(model_name)
if not response.found:
raise RuntimeError(
f"ModelExpress: no metadata found for model={model_name}"
)
# Find the worker matching our tp_rank
source_worker = None
for w in response.workers:
if w.worker_rank == tp_rank:
source_worker = w
break
if source_worker is None:
raise RuntimeError(
f"ModelExpress: no worker metadata for rank={tp_rank}"
)
# Extract session_id from oneof backend_metadata
backend_field = source_worker.WhichOneof("backend_metadata")
if backend_field == "transfer_engine_session_id":
seed_session_id = source_worker.transfer_engine_session_id
else:
raise RuntimeError(
f"ModelExpress: expected transfer_engine_session_id, "
f"got backend_metadata={backend_field}"
)
# Build {name: (addr, size_bytes)} from seed tensor descriptors
seed_weight_info = {}
for td in source_worker.tensors:
seed_weight_info[td.name] = (td.addr, td.size)
logger.info(
"ModelExpress: got %d tensor descriptors from seed (session=%s)",
len(seed_weight_info),
seed_session_id,
)
finally:
mx_client.close()
# Transfer weights via TransferEngine RDMA
seed_ptr_list = []
client_ptr_list = []
client_len_list = []
for name, tensor in model.named_parameters():
weight_info = seed_weight_info.get(name, None)
if weight_info is None:
raise RuntimeError(
f"ModelExpress: cannot find weight info for {name} "
f"in seed metadata"
)
seed_ptr, seed_size = weight_info
local_size = tensor.numel() * tensor.element_size()
if seed_size != local_size:
raise RuntimeError(
f"ModelExpress: size mismatch for {name}: "
f"seed={seed_size} bytes, local={local_size} bytes"
)
seed_ptr_list.append(seed_ptr)
client_ptr_list.append(tensor.data_ptr())
client_len_list.append(local_size)
logger.info(
"ModelExpress: starting RDMA transfer of %d tensors...",
len(seed_ptr_list),
)
ret = transfer_engine.batch_transfer_sync_read(
seed_session_id,
client_ptr_list,
seed_ptr_list,
client_len_list,
)
if ret < 0:
raise RuntimeError(
f"ModelExpress: batch_transfer_sync_read failed, error={ret}"
)
if hasattr(model, "post_load_weights"):
model.post_load_weights()
logger.info("ModelExpress: weight transfer complete for tp_rank=%d", tp_rank)
class RemoteModelLoader(BaseModelLoader):
"""Model loader that can load Tensors from remote database."""

1
python/sglang/srt/model_loader/remote_instance_weight_loader_utils.py
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@@ -15,6 +15,7 @@ logger = logging.getLogger(__name__)
class RemoteInstanceWeightLoaderBackend(str, enum.Enum):
NCCL = "nccl"
TRANSFER_ENGINE = "transfer_engine"
MODELEXPRESS = "modelexpress"
def trigger_init_weights_send_group_for_remote_instance_request(

67
python/sglang/srt/server_args.py
View File

@@ -700,8 +700,11 @@ class ServerArgs:
remote_instance_weight_loader_seed_instance_ip: Optional[str] = None
remote_instance_weight_loader_seed_instance_service_port: Optional[int] = None
remote_instance_weight_loader_send_weights_group_ports: Optional[List[int]] = None
remote_instance_weight_loader_backend: Literal["transfer_engine", "nccl"] = "nccl"
remote_instance_weight_loader_backend: Literal[
"transfer_engine", "nccl", "modelexpress"
] = "nccl"
remote_instance_weight_loader_start_seed_via_transfer_engine: bool = False
modelexpress_config: Optional[str] = None
# For PD-Multiplexing
enable_pdmux: bool = False
@@ -2967,7 +2970,19 @@ class ServerArgs:
self.custom_weight_loader = []
if self.load_format == "remote_instance":
if (
if self.remote_instance_weight_loader_backend == "modelexpress":
# ModelExpress backend: requires url in --modelexpress-config
if self.modelexpress_url is None:
logger.warning(
"Fallback load_format to 'auto' due to missing 'url' in --modelexpress-config."
)
self.load_format = "auto"
elif not self.validate_transfer_engine():
logger.warning(
"Fallback load_format to 'auto' due to 'transfer_engine' (required by modelexpress) not being supported."
)
self.load_format = "auto"
elif (
self.remote_instance_weight_loader_seed_instance_ip is None
or self.remote_instance_weight_loader_seed_instance_service_port is None
):
@@ -5567,15 +5582,21 @@ class ServerArgs:
parser.add_argument(
"--remote-instance-weight-loader-backend",
type=str,
choices=["transfer_engine", "nccl"],
choices=["transfer_engine", "nccl", "modelexpress"],
default=ServerArgs.remote_instance_weight_loader_backend,
help="The backend for loading weights from remote instance. Can be 'transfer_engine' or 'nccl'. Default is 'nccl'.",
help="The backend for loading weights from remote instance. Can be 'transfer_engine', 'nccl', or 'modelexpress'. Default is 'nccl'.",
)
parser.add_argument(
"--remote-instance-weight-loader-start-seed-via-transfer-engine",
action="store_true",
help="Start seed server via transfer engine backend for remote instance weight loader.",
)
parser.add_argument(
"--modelexpress-config",
type=str,
default=ServerArgs.modelexpress_config,
help='JSON config for ModelExpress P2P weight loading. Keys: "url" (required, gRPC host:port), "model_name" (optional, defaults to --model-path), "source" (optional bool, true for seed mode). Example: \'{"url": "localhost:8001", "model_name": "my-model", "source": true}\'',
)
# For PD-Multiplexing
parser.add_argument(
@@ -6109,7 +6130,11 @@ class ServerArgs:
)
def validate_transfer_engine(self):
if importlib.util.find_spec("mooncake.engine") is None:
try:
mooncake_available = importlib.util.find_spec("mooncake.engine") is not None
except (ModuleNotFoundError, ValueError):
mooncake_available = False
if not mooncake_available:
logger.warning(
"Failed to import mooncake.engine. Does not support using TransferEngine as remote instance weight loader backend."
)
@@ -6122,14 +6147,44 @@ class ServerArgs:
else:
return True
@property
def _parsed_modelexpress_config(self) -> dict:
cache = getattr(self, "_mx_config_cache", None)
if cache is not None:
return cache
if self.modelexpress_config is None:
result = {}
elif isinstance(self.modelexpress_config, str):
result = json.loads(self.modelexpress_config)
else:
result = self.modelexpress_config
object.__setattr__(self, "_mx_config_cache", result)
return result
@property
def modelexpress_url(self) -> Optional[str]:
return self._parsed_modelexpress_config.get("url")
@property
def modelexpress_model_name(self) -> Optional[str]:
return self._parsed_modelexpress_config.get("model_name")
@property
def modelexpress_source(self) -> bool:
return self._parsed_modelexpress_config.get("source", False)
def remote_instance_weight_loader_use_transfer_engine(self):
# Use TransferEngine as seed backend.
if self.remote_instance_weight_loader_start_seed_via_transfer_engine:
return True
# ModelExpress source mode also needs TransferEngine init.
if self.modelexpress_source:
return True
# Use TransferEngine as client backend.
elif (
self.load_format == "remote_instance"
and self.remote_instance_weight_loader_backend == "transfer_engine"
and self.remote_instance_weight_loader_backend
in ("transfer_engine", "modelexpress")
):
return True
else: