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[Feature] support model weight update in ep (#3765)

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* support model weight update in ep

* support model weight update in ep

* support model weight update in ep

* support model weight update in ep

* Update fused_moe_backend_base.py

* Update worker_process.py

* Update worker_process.py

* Update dynamic_weight_manager.py
This commit is contained in:
ltd0924
2025-09-02 17:16:03 +08:00
committed by GitHub
parent 1908465542
commit 905d89e42f
5 changed files with 47 additions and 22 deletions
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35
fastdeploy/worker/worker_process.py
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@@ -254,27 +254,26 @@ class PaddleDisWorkerProc:
"""
# Currently, only support single node
self.nnode = int((self.parallel_config.tensor_parallel_size + 7) // 8)
mp_num_per_node = self.parallel_config.tensor_parallel_size // self.nnode
req_ids = []
num_running_requests = 0
local_rank = self.local_rank % self.parallel_config.tensor_parallel_size
while True:
if self.local_rank == 0:
if self.model_weights_status.value[0] != 0:
self.exist_task_signal.value[0] = 2
else:
self.exist_task_signal.value[0] = 0
if self.parallel_config.tensor_parallel_size > 1:
# Synchronize before updating weights
paddle.distributed.barrier(self.parallel_config.tp_group)
self.model_weights_signal = paddle.zeros([1], dtype=paddle.int32)
while True:
if self.local_rank % self.parallel_config.tensor_parallel_size == 0:
if self.model_weights_status.value[0] != 0:
self.model_weights_signal[0] = int(self.model_weights_status.value[0])
if self.fd_config.load_config.dynamic_load_weight and self.parallel_config.enable_expert_parallel:
paddle.distributed.broadcast(self.model_weights_signal, src=0, group=self.parallel_config.ep_group)
if self.fd_config.load_config.dynamic_load_weight:
paddle.distributed.broadcast(self.model_weights_signal, src=0, group=self.parallel_config.tp_group)
self.insert_step = False
req_dicts = None
local_rank = self.local_rank % self.parallel_config.tensor_parallel_size
self.worker_healthy_live_signal.value[local_rank % self.max_chips_per_node] = int(time.time())
# The first worker detects whether there are tasks in the task queue
if self.local_rank % mp_num_per_node == 0:
if self.local_rank % self.parallel_config.tensor_parallel_size == 0:
if self.task_queue.num_tasks() > 0:
# VL only support 1 batch to prefill
if envs.ENABLE_V1_KVCACHE_SCHEDULER or not (
@@ -290,16 +289,24 @@ class PaddleDisWorkerProc:
paddle.distributed.barrier(self.parallel_config.tp_group)
if self.fd_config.load_config.dynamic_load_weight:
if self.exist_task_signal.value[0] == 2:
if self.parallel_config.enable_expert_parallel:
paddle.distributed.barrier(self.parallel_config.ep_group)
else:
paddle.distributed.barrier(self.parallel_config.tp_group)
if self.model_weights_signal[0] != 0:
logger.info(f"Rank: {self.local_rank} has updated parameters.")
from fastdeploy.rl.dynamic_weight_manager import (
DynamicWeightManager,
)
self.model_weights_status.value[0] = self.model_weights_signal[0]
DynamicWeightManager.check_model_weights_status(
self.model_weights_status,
# model_weights_signal
self.worker.model_runner,
self.parallel_config.engine_pid,
self.parallel_config.engine_worker_queue_port,
)
self.model_weights_signal[0] = 0
if self.exist_task_signal.value[0] == 1 or self.task_queue.read_finish_flag.get() == 1:
logger.info(f"Rank: {self.local_rank} Detected new requests.")