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Fix dtype-aware IPC buffers for RL colocated weight updates #1789

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Merged
hhaAndroid merged 1 commit into from
May 15, 2026
+58 −62
Merged

Fix dtype-aware IPC buffers for RL colocated weight updates #1789

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120 changes: 58 additions & 62 deletions xtuner/v1/rl/trainer/update_weighter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,8 +62,10 @@ def _init_update_weighter(self):
self.worker_server_urls_status: dict[str, bool] = {}

self._global_hf_keys_mapping_cache: dict[str, list[str]] = dict()
self._ipc_tensor_bytes: int = int(self.config.update_weight_bucket_size_in_gb * 1024**3)
self._update_params_ipc_tensor = None
self._default_ipc_tensor_bytes: int = int(self.config.update_weight_bucket_size_in_gb * 1024**3)
self._ipc_tensor_bytes_dict_by_dtype: dict[torch.dtype, int] = {}
self._update_params_ipc_tensor_dict_by_dtype: dict[torch.dtype, torch.Tensor] = {}
self._last_update_params_ipc_tensor_dtype: torch.dtype | None = None
self._update_params_ipc_event = None
self._sglang_disagg_group: dist.ProcessGroup | None = None
self._sglang_disagg_group_name: str | None = None
Expand Down Expand Up @@ -187,7 +189,8 @@ def _update_weights_colocated(self):
self._update_weights_hf_generator(submodule="multi_modal_projector", final_update=True)
else:
self._update_weights_hf_generator(final_update=True)
self._update_params_ipc_tensor = None
self._update_params_ipc_tensor_dict_by_dtype = {}
self._last_update_params_ipc_tensor_dtype = None
self._update_params_ipc_event = None

DEVICE_MODULE.empty_cache()
Expand Down Expand Up @@ -508,6 +511,52 @@ def _init_external_process_group(
def _create_ipc_tensor(size_in_bytes: int, dtype: torch.dtype):
return torch.empty(size_in_bytes, dtype=torch.uint8, device=DEVICE).view(dtype)

def _build_lmdeploy_flattened_tensor_data(self, state_dict: dict, flattened_tensor_bucket_cls) -> dict:
# LMDeploy flattened buckets require all tensors in one bucket to share a dtype.
state_dict_dtype = state_dict[next(iter(state_dict))].dtype
update_params_ipc_tensor = self._update_params_ipc_tensor_dict_by_dtype.get(state_dict_dtype, None)
state_dict_bytes = self._compute_state_dict_bytes(state_dict)
ipc_tensor_bytes = self._ipc_tensor_bytes_dict_by_dtype.get(
state_dict_dtype,
self._default_ipc_tensor_bytes,
)
dtype_changed = (
self._last_update_params_ipc_tensor_dtype is not None
and state_dict_dtype != self._last_update_params_ipc_tensor_dtype
)
need_resize = state_dict_bytes > ipc_tensor_bytes
send_ipc_tensor = dtype_changed or need_resize or update_params_ipc_tensor is None

if update_params_ipc_tensor is not None:
self._update_params_ipc_event.wait()
if need_resize:
torch.cuda.synchronize()

if update_params_ipc_tensor is None or need_resize:
ipc_tensor_bytes = max(ipc_tensor_bytes, state_dict_bytes)
self._ipc_tensor_bytes_dict_by_dtype[state_dict_dtype] = ipc_tensor_bytes
update_params_ipc_tensor = self._create_ipc_tensor(
ipc_tensor_bytes,
state_dict_dtype,
)
self._update_params_ipc_tensor_dict_by_dtype[state_dict_dtype] = update_params_ipc_tensor

flattened_tensor_bucket = flattened_tensor_bucket_cls(
named_tensors=list(state_dict.items()),
flattened_tensor=update_params_ipc_tensor,
)
flattened_tensor_data = {
"metadata": flattened_tensor_bucket.get_metadata(),
"require_clone": False,
}
self._update_params_ipc_event.record()
self._last_update_params_ipc_tensor_dtype = state_dict_dtype

if send_ipc_tensor:
flattened_tensor_data["flattened_tensor"] = flattened_tensor_bucket.get_flattened_tensor()
flattened_tensor_data["event_ipc_handle"] = self._update_params_ipc_event.ipc_handle()
return flattened_tensor_data

def _get_sglang_disagg_engine_info(self) -> RolloutEngineInfo:
engine_info: RolloutEngineInfo = []
seen_urls: set[str] = set()
Expand Down Expand Up @@ -744,36 +793,10 @@ def serialize_state_dict(state_dict: dict) -> str:
if self.rollout_cfg_info["backend"] == "pytorch" and self.rollout_cfg_info["tp"] > 1:
serialized_data = [None] * self.rollout_cfg_info["tp"]
if use_flattened_tensor_bucket and state_dict:
state_dict_bytes = self._compute_state_dict_bytes(state_dict)
send_ipc_tensor = (
state_dict_bytes > self._ipc_tensor_bytes or self._update_params_ipc_tensor is None
flattened_tensor_data = self._build_lmdeploy_flattened_tensor_data(
state_dict,
FlattenedTensorBucket,
)
if send_ipc_tensor:
self._ipc_tensor_bytes = max(self._ipc_tensor_bytes, state_dict_bytes)
if self._update_params_ipc_tensor is not None:
self._update_params_ipc_event.wait()
torch.cuda.synchronize()
self._update_params_ipc_tensor = self._create_ipc_tensor(
self._ipc_tensor_bytes,
state_dict[next(iter(state_dict))].dtype,
)
else:
self._update_params_ipc_event.wait()

flattened_tensor_bucket = FlattenedTensorBucket(
named_tensors=list(state_dict.items()),
flattened_tensor=self._update_params_ipc_tensor,
)
metadata = flattened_tensor_bucket.get_metadata()
flattened_tensor_data = {
"metadata": metadata,
"require_clone": False,
}
self._update_params_ipc_event.record()

if send_ipc_tensor:
flattened_tensor_data["flattened_tensor"] = flattened_tensor_bucket.get_flattened_tensor()
flattened_tensor_data["event_ipc_handle"] = self._update_params_ipc_event.ipc_handle()
tp_serialized_data = serialize_state_dict(flattened_tensor_data)
else:
tp_serialized_data = serialize_state_dict(state_dict)
Expand All @@ -785,37 +808,10 @@ def serialize_state_dict(state_dict: dict) -> str:
)
elif self.rollout_cfg_info["backend"] == "pytorch":
if use_flattened_tensor_bucket and state_dict:
state_dict_bytes = self._compute_state_dict_bytes(state_dict)
send_ipc_tensor = (
state_dict_bytes > self._ipc_tensor_bytes or self._update_params_ipc_tensor is None
)
if send_ipc_tensor:
self._ipc_tensor_bytes = max(self._ipc_tensor_bytes, state_dict_bytes)
if self._update_params_ipc_tensor is not None:
# wait previous ipc event recorded of lmdeploy
self._update_params_ipc_event.wait()
torch.cuda.synchronize()
self._update_params_ipc_tensor = self._create_ipc_tensor(
self._ipc_tensor_bytes,
state_dict[next(iter(state_dict))].dtype,
)
else:
self._update_params_ipc_event.wait()

flattened_tensor_bucket = FlattenedTensorBucket(
named_tensors=list(state_dict.items()),
flattened_tensor=self._update_params_ipc_tensor,
flattened_tensor_data = self._build_lmdeploy_flattened_tensor_data(
state_dict,
FlattenedTensorBucket,
)
metadata = flattened_tensor_bucket.get_metadata()
flattened_tensor_data = {
"metadata": metadata,
"require_clone": False,
}
self._update_params_ipc_event.record()

if send_ipc_tensor:
flattened_tensor_data["flattened_tensor"] = flattened_tensor_bucket.get_flattened_tensor()
flattened_tensor_data["event_ipc_handle"] = self._update_params_ipc_event.ipc_handle()
serialized_data = serialize_state_dict(flattened_tensor_data)
else:
serialized_data = serialize_state_dict(state_dict)
Expand Down
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