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[NewFeture]add ep rollout model init and update/clear ep buffer (#3927)

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* add ep rollout model init && add deep update/clear

* fix test
This commit is contained in:
gaoziyuan
2025-09-12 14:15:13 +08:00
committed by GitHub
parent c64ceac34d
commit 10768a4d79
13 changed files with 364 additions and 304 deletions
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45
fastdeploy/rl/dynamic_weight_manager.py
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@@ -48,7 +48,7 @@ class DynamicWeightManager:
logger.info(
f"✅ DynamicLoad model built successfully by {self.load_config.load_strategy}, "
f" rank={self.rank}, ranks={self.nranks}"
f" tp rank={self.rank}, dp rank={fd_config.parallel_config.local_data_parallel_id}, ep rank={fd_config.parallel_config.expert_parallel_rank}, ranks={self.nranks}, "
)
@paddle.no_grad()
@@ -63,11 +63,21 @@ class DynamicWeightManager:
start_time = time.perf_counter()
paddle.device.cuda.empty_cache()
# step1 : restart paddle process group
if not self.first_load:
paddle.distributed.restart_process_group(self.parallel_config.tp_group)
if self.parallel_config.enable_expert_parallel:
paddle.distributed.restart_process_group(self.parallel_config.ep_group)
# step2 : recreat deepep buffer when enable expert parallel
if self.parallel_config.enable_expert_parallel and not self.first_load:
from fastdeploy.model_executor.layers.moe.ep import DeepEPBufferManager
DeepEPBufferManager.recreate_buffer()
# ep barrier
paddle.distributed.barrier(self.parallel_config.ep_group)
# step3 : update model weight
strategy_handlers = {
"ipc_snapshot": self._update_ipc_snapshot,
"ipc": self._update_ipc,
@@ -79,6 +89,10 @@ class DynamicWeightManager:
raise ValueError(f"Unsupported strategy: {self.load_config.load_strategy}")
logger.info(f"Update parameters in {time.perf_counter()-start_time:.2f}s")
# steps in the runner
# step 4: reinitialze kv_cache
# step 5: recapture CUDAGraph
# step 6: update weight status signal
def _update_ipc_snapshot(self):
"""Update using IPC snapshot strategy for elastic recovery."""
@@ -106,18 +120,31 @@ class DynamicWeightManager:
def clear_parameters(self, pid: int = 0) -> None:
"""Clear all model parameters and free memory."""
logger.info("start clear paramaters")
# step1: release deepep buffer
if self.parallel_config.enable_expert_parallel:
from fastdeploy.model_executor.layers.moe.ep import DeepEPBufferManager
DeepEPBufferManager.clear_buffer()
# ep barrier
paddle.distributed.barrier(self.parallel_config.ep_group)
# shutdown ep group
paddle.distributed.shutdown_process_group(self.parallel_config.ep_group)
paddle.device.cuda.empty_cache()
# step2: release model weight
for param in self.model.state_dict().values():
param._clear_data()
self._verify_parameters("clearance")
if self.parallel_config.tensor_parallel_size > 1:
# tp barrier
paddle.distributed.barrier(self.parallel_config.tp_group)
paddle.distributed.shutdown_process_group(self.parallel_config.tp_group)
if self.parallel_config.enable_expert_parallel:
paddle.distributed.barrier(self.parallel_config.ep_group)
paddle.distributed.shutdown_process_group(self.parallel_config.ep_group)
paddle.distributed.shutdown_process_group()
# shutdown tp group
paddle.distributed.shutdown_process_group(self.parallel_config.tp_group)
# step3: update model weight signal
# step4: release kv cache in the runner
self._update_shared_status(pid, -2)
def _update_model_from_state(self, state_dict: Dict[str, paddle.Tensor], src_type: str):
@@ -146,10 +173,16 @@ class DynamicWeightManager:
def finalize_update(self, pid: int = 0):
"""Finalize update process with verification."""
self._verify_parameters("update")
if self.parallel_config.tensor_parallel_size > 1:
paddle.distributed.barrier(self.parallel_config.tp_group)
if self.parallel_config.enable_expert_parallel:
paddle.distributed.barrier(self.parallel_config.ep_group)
if not self.first_load:
self._update_shared_status(pid, 0)
self.first_load = False
def _get_gpu_id(self) -> int: