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fix: resolve HCCL port conflict and LoRA weight update issues #422

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fix: resolve HCCL port conflict and LoRA weight update issues #422

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3377bb8
fix: resolve HCCL port conflict in colocate mode for NPU
Ginray Apr 10, 2026
8dde7b1
Merge remote-tracking branch 'origin/main' into fix/hccl-port-conflic…
Ginray Apr 10, 2026
7ccf58b
fix: resolve LoRA weight parameter name prefix issue for vLLM
Ginray Apr 11, 2026
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3 changes: 3 additions & 0 deletions roll/distributed/executor/cluster.py
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Original file line number Diff line number Diff line change
Expand Up @@ -140,6 +140,9 @@ def _create_workers(self):
if "ROLL_LOG_DIR" in os.environ:
env_vars["ROLL_LOG_DIR"] = os.environ["ROLL_LOG_DIR"]
env_vars.update(self.worker_config.system_envs)
if current_platform.is_npu():
env_vars["HCCL_HOST_SOCKET_PORT_RANGE"] = "auto"
env_vars["HCCL_NPU_SOCKET_PORT_RANGE"] = "auto"

runtime_env = RuntimeEnv(env_vars=env_vars)
self.worker_config.resource_placement_groups = pgs
Expand Down
28 changes: 23 additions & 5 deletions roll/third_party/deepspeed/model_update.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,9 +29,26 @@ def _gather_weights(is_zero3, named_params):
return [(n, p.data) for n, p in named_params]


def gather_deepspeed_weights(model, ds_config, buffer_size):
def gather_deepspeed_weights(model, ds_config, buffer_size, is_lora=False):
is_zero3 = ds_config.is_zero3()
named_params = [(name, param) for name, param in model.named_parameters()]
if is_lora:
if not is_zero3:
from peft.utils import get_peft_model_state_dict
lora_state_dict = get_peft_model_state_dict(model)
named_params = [(name, param) for name, param in lora_state_dict.items()]
else:
adapter_name = "default"
state_dict = model.state_dict()
lora_state_dict = {k: state_dict[k] for k in state_dict if ("lora_" in k and adapter_name in k)}
named_params = []
for name, param in lora_state_dict.items():
clean_name = name.replace(f".{adapter_name}", "")
if clean_name.startswith("base_model.model."):
clean_name = clean_name[len("base_model.model."):]
named_params.append((clean_name, model.get_parameter(name)))
del lora_state_dict
else:
named_params = [(name, param) for name, param in model.named_parameters()]

waiting_params, waiting_params_size = [], 0
for name, param in named_params:
Expand Down Expand Up @@ -150,7 +167,7 @@ def _setup_broadcast_group(self):
def _colocated_model_update(self):
refs = []
for named_weights in gather_deepspeed_weights(
self.model, self.ds_config, buffer_size=self._model_update_buffer_size
self.model, self.ds_config, buffer_size=self._model_update_buffer_size, is_lora=self.is_lora
):
serialized_tensors = serialize_named_weights(
named_weights, infer_strategy=self.infer_worker_config.strategy_args.strategy_name
Expand All @@ -167,7 +184,7 @@ def _colocated_model_update(self):
ray.get(refs)
refs = []
if co_infer_rank == 0 and self._co_infer_worker is not None:
refs.append(self._co_infer_worker.update_parameter_in_bucket.remote(infer_parallel_tensors))
refs.append(self._co_infer_worker.update_parameter_in_bucket.remote(infer_parallel_tensors, is_lora=self.is_lora))
if self._broadcast_workers:
refs.extend(self._broadcast_to_infer_workers(named_weights))
if refs:
Expand All @@ -183,6 +200,7 @@ def _broadcast_to_infer_workers(self, named_weights) -> list[ray.ObjectRef]:
names=[n for n, _ in named_weights],
dtypes=[w.dtype for _, w in named_weights],
shapes=[w.shape for _, w in named_weights],
is_lora=self.is_lora,
)
for worker in self._broadcast_workers
]
Expand All @@ -198,7 +216,7 @@ def _broadcast_to_infer_workers(self, named_weights) -> list[ray.ObjectRef]:
def _separated_model_update(self):
logger.info(f"start broadcast model update {self.model_update_group_name}")
for named_weights in gather_deepspeed_weights(
self.model, self.ds_config, buffer_size=self._model_update_buffer_size
self.model, self.ds_config, buffer_size=self._model_update_buffer_size, is_lora=self.is_lora
):
refs = self._broadcast_to_infer_workers(named_weights)
ray.get(refs)
Expand Down