[xpu] use cpu barrier (#4181)

This commit is contained in:
zhupengyang
2025-09-23 12:19:03 +08:00
committed by GitHub
parent 813befadfa
commit 9082f625ba
2 changed files with 17 additions and 2 deletions

View File

@@ -101,6 +101,9 @@ class EngineWorkerQueue:
self.finish_request_barrier = [
threading.Barrier(self.num_client) for _ in range(self.local_data_parallel_size)
]
self.worker_process_tp_barrier = [
threading.Barrier(self.num_client) for _ in range(self.local_data_parallel_size)
]
self.finish_add_cache_task_barrier = [
threading.Barrier(self.num_client) for _ in range(self.local_data_parallel_size)
@@ -193,6 +196,10 @@ class EngineWorkerQueue:
"get_finish_add_cache_task_barrier",
callable=lambda idx: self.finish_add_cache_task_barrier[idx],
)
QueueManager.register(
"get_worker_process_tp_barrier",
callable=lambda idx: self.worker_process_tp_barrier[idx],
)
self.manager: BaseManager = QueueManager(address=self.address, authkey=self.authkey)
self.manager.start()
else:
@@ -217,6 +224,7 @@ class EngineWorkerQueue:
QueueManager.register("get_connect_rdma_tasks")
QueueManager.register("get_connect_rdma_tasks_responses")
QueueManager.register("get_connect_task_lock")
QueueManager.register("get_worker_process_tp_barrier")
self.manager = QueueManager(address=self.address, authkey=self.authkey)
self._connect_with_retry()
@@ -239,6 +247,7 @@ class EngineWorkerQueue:
self.finish_add_cache_task_barrier = self.manager.get_finish_add_cache_task_barrier(
self.local_data_parallel_id
)
self.worker_process_tp_barrier = self.manager.get_worker_process_tp_barrier(self.local_data_parallel_id)
self.finished_req_queue = self.manager.get_finish_request_queue(self.local_data_parallel_id)
self.finished_add_cache_task_queue = self.manager.get_finish_add_cache_task_queue(
self.local_data_parallel_id

View File

@@ -256,6 +256,12 @@ class PaddleDisWorkerProc:
paddle.distributed.broadcast(model_weights_signal_tensor, src=src, group=group)
return model_weights_signal_tensor.item()
def _tp_barrier_wait(self):
if current_platform.is_xpu():
self.task_queue.worker_process_tp_barrier.wait()
else:
paddle.distributed.barrier(self.parallel_config.tp_group)
def event_loop_normal(self) -> None:
"""Main event loop for Paddle Distributed Workers.
TODO(gongshaotian): support remote calling of functions that control worker.
@@ -299,7 +305,7 @@ class PaddleDisWorkerProc:
if self.parallel_config.tensor_parallel_size > 1:
# Synchronize the signal for other workers
paddle.distributed.barrier(self.parallel_config.tp_group)
self._tp_barrier_wait()
if self.fd_config.load_config.dynamic_load_weight:
if self.parallel_config.enable_expert_parallel:
@@ -350,7 +356,7 @@ class PaddleDisWorkerProc:
if (not self.parallel_config.use_ep) and (not self.worker.model_runner.not_need_stop()):
if self.ranks > 1:
paddle.distributed.barrier(self.parallel_config.tp_group)
self._tp_barrier_wait()
time.sleep(0.001)
continue