o * i, @sddlmZddlmZddlZddlmZddlmZer6ddlm Z ddl m Z ddl m Z dd lmZejdd fdddZdS)) annotations) TYPE_CHECKINGN)stream)ReduceOp)Tensor)task)Group) _ReduceOpTtensorropr group Group | Nonesync_opboolreturnrcCsl|tjkr,tjjdkr,|durtjjn|}| d|j t j |tj ||ddSt j ||||ddS)aD 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|ReduceOp.AVG, optional): The operation used. Default value is ReduceOp.SUM. group (Group|None, optional): The group instance return by new_group or None for global default group. sync_op (bool, optional): Whether 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) iRNg?F)r r rZuse_calc_stream)rZAVGpaddlebasecoreZ nccl_version distributedZ collectiveZ_get_global_groupZscale_Znranksr all_reduceSUM)r r r rrw/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/paddle/distributed/communication/all_reduce.pyrs -  r) r rr r r r rrrr) __future__rtypingrrZ paddle.distributed.communicationrZ'paddle.distributed.communication.reducerrZpaddle.base.corerZ&paddle.distributed.communication.grouprr rrrrrrs