o "j @s.ddlmZddlmZejddfddZdS))stream)ReduceOpNTcCstj||||ddS)a1 Reduce a tensor over all ranks so that all get the result. As shown below, one process is started with a GPU and the data of this process is represented by its group rank. The reduce operator is sum. Through all_reduce operator, each GPU will have the sum of the data from all GPUs. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/allreduce.png :width: 800 :alt: all_reduce :align: center Args: tensor (Tensor): The input Tensor. It also works as the output Tensor. Its data type should be float16, float32, float64, int32, int64, int8, uint8 or bool. op (ReduceOp.SUM|ReduceOp.MAX|ReduceOp.MIN|ReduceOp.PROD, optional): The operation used. Default value is ReduceOp.SUM. group (Group, optional): The group instance return by new_group or None for global default group. sync_op (bool, optional): Wether this op is a sync op. Default value is True. Returns: Return a task object. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env: DISTRIBUTED) >>> import paddle >>> import paddle.distributed as dist >>> dist.init_parallel_env() >>> if dist.get_rank() == 0: ... data = paddle.to_tensor([[4, 5, 6], [4, 5, 6]]) >>> else: ... data = paddle.to_tensor([[1, 2, 3], [1, 2, 3]]) >>> dist.all_reduce(data) >>> print(data) >>> # [[5, 7, 9], [5, 7, 9]] (2 GPUs) F)opgroupsync_opZuse_calc_stream)r all_reduce)Ztensorrrrrl/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/distributed/communication/all_reduce.pyrs' r)Z paddle.distributed.communicationrZ'paddle.distributed.communication.reducerZSUMrrrrr s