Skip to content
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
64 changes: 60 additions & 4 deletions nemo_rl/models/generation/vllm.py
Original file line number Diff line number Diff line change
Expand Up @@ -347,6 +347,16 @@ def _patch_vllm_init_workers_ray():
else:
self.llm = vllm.LLM(**llm_kwargs)

# will be initialized in post_init
# used in update_weights_from_ipc_handles
self.vllm_device_ids = None

def post_init(self):
self.vllm_device_ids = self.report_device_id()

async def post_init_async(self):
self.vllm_device_ids = await self.report_device_id_async()

def init_collective(
self, rank_prefix: int, ip: str, port: int, world_size: int
) -> None:
Expand Down Expand Up @@ -1030,9 +1040,37 @@ def update_weights_from_ipc_handles(self, ipc_handles: dict[str, Any]) -> bool:
"update_weights_from_ipc_handles cannot be used with async_engine=True. Use update_weights_from_ipc_handles_async instead."
)

result_or_coro = self.llm.collective_rpc(
"update_weights_from_ipc_handles", args=(ipc_handles,)
)
if self.tensor_parallel_size == 1:
# UniProcExecutor
assert len(self.vllm_device_ids) == 1
result_or_coro = self.llm.collective_rpc(
"update_weights_from_local_ipc_handles",
args=(ipc_handles[self.vllm_device_ids[0]],),
)
else:
"""
DO NOT USE VLLM's collective_rpc: This code causes duplicate IPC data transfer across Ray workers,
leading to unnecessary network serialization overhead and potential performance degradation.

result_or_coro = self.llm.collective_rpc(
"update_weights_from_global_ipc_handles", args=(ipc_handles,)
)
"""
ray_worker_outputs = []
# MultiProcExecutor
for worker, device_id in zip(
self.llm.llm_engine.model_executor.workers, self.vllm_device_ids
):
ray_worker_outputs.append(
worker.execute_method.remote(
"update_weights_from_local_ipc_handles",
ipc_handles[device_id],
)
)

# Gather the results
result_or_coro = ray.get(ray_worker_outputs)

worker_result = result_or_coro[0]

if not worker_result:
Expand Down Expand Up @@ -1069,8 +1107,9 @@ async def update_weights_from_ipc_handles_async(
"update_weights_from_ipc_handles_async can only be used with async_engine=True. Use update_weights_from_ipc_handles instead."
)

# TODO: switch to update_weights_from_local_ipc_handles for better performance once collectively report_device_id is supported in asyncLLM initialization
result_or_coro = await self.llm.collective_rpc(
"update_weights_from_ipc_handles", args=(ipc_handles,)
"update_weights_from_global_ipc_handles", args=(ipc_handles,)
)

if asyncio.iscoroutine(result_or_coro):
Expand Down Expand Up @@ -1356,6 +1395,10 @@ def __init__(
env_vars=env_vars,
)

# Call some collective rpc functions in VllmGenerationWorker when initializing the vLLM engine
# This is necessary for async engine to work
self._post_init()

# Number of data parallel groups is the number of tied worker groups
self.dp_size = self.worker_group.dp_size

Expand Down Expand Up @@ -1496,6 +1539,19 @@ def _report_device_id(self) -> list[list[str]]:
results = ray.get(futures)
return results

def _post_init(self):
# Choose the appropriate method based on async_engine setting
method_name = (
"post_init_async" if self.cfg["vllm_cfg"]["async_engine"] else "post_init"
)
# Use run_all_workers_single_data for methods that don't need data
futures = self.worker_group.run_all_workers_single_data(
method_name, run_rank_0_only_axes=["tensor_parallel", "pipeline_parallel"]
)
# Wait for all futures to complete
results = ray.get(futures)
return results

def init_collective(
self, ip: str, port: int, world_size: int
) -> list[ray.ObjectRef]:
Expand Down
28 changes: 19 additions & 9 deletions nemo_rl/models/generation/vllm_backend.py
Original file line number Diff line number Diff line change
Expand Up @@ -51,24 +51,34 @@ def report_device_id(self) -> str:

return get_device_uuid(self.device.index)

def update_weights_from_ipc_handles(self, ipc_handles):
"""Update weights from IPC handles.
def update_weights_from_global_ipc_handles(self, global_device_ipc_handles):
"""Update weights from global IPC handles.

Args:
ipc_handles (dict): Dictionary mapping device UUIDs to parameter IPC handles.
global_device_ipc_handles (dict): Dictionary mapping device UUIDs to parameter IPC handles.

Returns:
bool: True if weights were successfully updated.
"""
device_uuid = self.report_device_id()
local_device_ipc_handles = global_device_ipc_handles[device_uuid]
return self.update_weights_from_local_ipc_handles(local_device_ipc_handles)

def update_weights_from_local_ipc_handles(self, local_device_ipc_handles):
"""Update weights from local IPC handles.

Args:
local_device_ipc_handles (dict): parameter IPC handles for local device.

Returns:
bool: True if weights were successfully updated.
"""
try:
# Get handles for this device
device_uuid = self.report_device_id()
handles = ipc_handles[device_uuid]
is_tensor_packed = handles[0]
is_tensor_packed = local_device_ipc_handles[0]
if is_tensor_packed:
_, all_handles, tensor_metadata = handles
_, all_handles, tensor_metadata = local_device_ipc_handles
else:
_, name_and_handle_list = handles
_, name_and_handle_list = local_device_ipc_handles

device_id = self.device.index
weights = []
Expand Down
Loading