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[feat] support prefix cache clearing when /clear_load_weight is called (#4091)
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Browse Source 
* [feat] support clearing prefix cache (cherry-picked from release/2.1)

* [fix] fix ipc suffix, use port instead

* [fix] fix prefix caching not enabled

* [fix] fix code style

* [fix] wait for rank0 to update weight status
This commit is contained in:
李泳桦
2025-09-16 11:11:20 +08:00
committed by GitHub
parent fbb4e0f8d1
commit 7ccbcc5a62
17 changed files with 624 additions and 181 deletions
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20
fastdeploy/rl/dynamic_weight_manager.py
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@@ -25,6 +25,7 @@ from paddle import nn
from paddleformers.utils.log import logger
from fastdeploy.config import FDConfig
from fastdeploy.inter_communicator import ModelWeightsStatus
class DynamicWeightManager:
@@ -59,6 +60,7 @@ class DynamicWeightManager:
def update_parameters(self, pid: int = 0) -> None:
"""Core method to update model parameters based on strategy."""
logger.info(f"start update paramaters: suffix={pid} rank={self.rank}")
start_time = time.perf_counter()
paddle.device.cuda.empty_cache()
@@ -106,7 +108,7 @@ class DynamicWeightManager:
def clear_parameters(self, pid: int = 0) -> None:
"""Clear all model parameters and free memory."""
logger.info("start clear paramaters")
logger.info(f"start clear paramaters: suffix={pid} rank={self.rank}")
paddle.device.cuda.empty_cache()
for param in self.model.state_dict().values():
param._clear_data()
@@ -119,7 +121,7 @@ class DynamicWeightManager:
paddle.distributed.barrier(self.parallel_config.ep_group)
paddle.distributed.shutdown_process_group(self.parallel_config.ep_group)
paddle.distributed.shutdown_process_group()
self._update_shared_status(pid, -2)
self._update_shared_status(pid, ModelWeightsStatus.CLEARED)
def _update_model_from_state(self, state_dict: Dict[str, paddle.Tensor], src_type: str):
"""Update model parameters from given state dictionary."""
@@ -150,7 +152,7 @@ class DynamicWeightManager:
if self.parallel_config.tensor_parallel_size > 1:
paddle.distributed.barrier(self.parallel_config.tp_group)
if not self.first_load:
self._update_shared_status(pid, 0)
self._update_shared_status(pid, ModelWeightsStatus.NORMAL)
self.first_load = False
def _get_gpu_id(self) -> int:
@@ -217,20 +219,20 @@ class DynamicWeightManager:
"""
check model weights status
"""
logger.info(f"dynamic weight manager is check model weights status! {model_weights_status.value[0]}")
is_stop = 0
while model_weights_status.value[0] != 0:
if model_weights_status.value[0] == 1:
while model_weights_status.value[0] != ModelWeightsStatus.NORMAL:
if model_weights_status.value[0] == ModelWeightsStatus.UPDATING:
logger.info("infer engine stopped! start to load new checkpoint...")
model_runner.update_parameters(pid)
elif model_weights_status.value[0] == -1:
elif model_weights_status.value[0] == ModelWeightsStatus.CLEARING:
logger.info("infer engine stopped! start to clear checkpoint...")
model_runner.clear_parameters(pid)
while True:
if model_weights_status.value[0] == 0:
if model_weights_status.value[0] == ModelWeightsStatus.NORMAL:
logger.info("finished loading new checkpoint")
break
elif is_stop == 1 or (model_weights_status.value[0] == -2 and is_stop == 0):
elif is_stop == 1 or (model_weights_status.value[0] == ModelWeightsStatus.CLEARED and is_stop == 0):
if is_stop == 0:
logger.info("finished clearing checkpoint")
is_stop = 1