ΑiSSKJr SSKJr SSKJr \(aSSKJr SSKJ r SSK J r S S Sjjr S S S jjr g)) annotations) TYPE_CHECKING)stream)Tensor)task)GroupNc2[R"XX#S5$)a0 Scatter tensors in in_tensor_list to all participators averagely and gather the result tensors in out_tensor_list. As shown below, the in_tensor_list in GPU0 includes 0_0 and 0_1, and GPU1 includes 1_0 and 1_1. Through alltoall operator, the 0_0 in GPU0 will be sent to GPU0 and 0_1 to GPU1, 1_0 in GPU1 sent to GPU0 and 1_1 to GPU1. Finally the out_tensor_list in GPU0 includes 0_0 and 1_0, and GPU1 includes 0_1 and 1_1. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/alltoall.png :width: 800 :alt: alltoall :align: center Args: out_tensor_list (List[Tensor]): List of tensors to be gathered one per rank. The data type of each tensor should be the same as the input tensors. in_tensor_list (List[Tensor]): List of tensors to scatter one per rank. The data type of each tensor should be float16, float32, float64, int32, int64, int8, uint8, bool or bfloat16. group (Group, optional): The group instance return by new_group or None for global default group. Default: None. sync_op (bool, optional): Whether this op is a sync op. The 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() >>> # all_to_all with equal split sizes >>> out_tensor_list = [] # type: ignore >>> if dist.get_rank() == 0: ... data1 = paddle.to_tensor([[1, 2, 3], [4, 5, 6]]) ... data2 = paddle.to_tensor([[7, 8, 9], [10, 11, 12]]) >>> else: ... data1 = paddle.to_tensor([[13, 14, 15], [16, 17, 18]]) ... data2 = paddle.to_tensor([[19, 20, 21], [22, 23, 24]]) >>> dist.alltoall(out_tensor_list, [data1, data2]) >>> print(out_tensor_list) >>> # [[[1, 2, 3], [4, 5, 6]], [[13, 14, 15], [16, 17, 18]]] (2 GPUs, out for rank 0) >>> # [[[7, 8, 9], [10, 11, 12]], [[19, 20, 21], [22, 23, 24]]] (2 GPUs, out for rank 1) >>> # all_to_all with unequal split sizes >>> if dist.get_rank() == 0: ... data1 = paddle.to_tensor([[1, 2, 3], [4, 5, 6]]) # shape: (2, 3) ... data2 = paddle.to_tensor([7]) # shape: (1, ) ... out_data1 = paddle.empty((2, 3), dtype=data1.dtype) ... out_data2 = paddle.empty((3, 2), dtype=data1.dtype) >>> else: ... data1 = paddle.to_tensor([[8, 9], [10, 11], [12, 13]]) # shape: (3, 2) ... data2 = paddle.to_tensor([[14, 15, 16, 17]]) # shape: (1, 4) ... out_data1 = paddle.empty((1,), dtype=data1.dtype) ... out_data2 = paddle.empty((1, 4), dtype=data1.dtype) >>> dist.alltoall([out_data1, out_data2], [data1, data2]) >>> print([out_data1, out_data2]) >>> # [[[1, 2, 3], [4, 5, 6]], [[8, 9], [10, 11], [12, 13]]] (2 GPUs, out for rank 0) >>> # [[7], [[14, 15, 16, 17]]] (2 GPUs, out for rank 1) F)ralltoall)out_tensor_listin_tensor_listgroupsync_ops k/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/distributed/communication/all_to_all.pyr r sB ?? c :[R"UUUUUUS5$)a Scatter a single input tensor to all participators and gather the received tensors in out_tensor. Note: ``alltoall_single`` is only supported in eager mode. Args: out_tensor (Tensor): Output Tensor. The data type should be the same as the data type of the input Tensor. in_tensor (Tensor): Input tensor. The data type should be float16, float32, float64, int32, int64, int8, uint8, bool or bfloat16. in_split_sizes (list[int]|None, optional): Split sizes of ``in_tensor`` for dim[0]. If not given, dim[0] of ``in_tensor`` must be divisible by group size and ``in_tensor`` will be scattered averagely to all participators. Default: None. out_split_sizes (list[int]|None, optional): Split sizes of ``out_tensor`` for dim[0]. If not given, dim[0] of ``out_tensor`` must be divisible by group size and ``out_tensor`` will be gathered averagely from all participators. Default: None. group (Group|None, optional): The group instance return by ``new_group`` or None for global default group. Default: None. sync_op (bool, optional): Whether this op is a sync op. The 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() >>> rank = dist.get_rank() >>> size = dist.get_world_size() >>> # case 1 (2 GPUs) >>> data = paddle.arange(2, dtype='int64') + rank * 2 >>> # data for rank 0: [0, 1] >>> # data for rank 1: [2, 3] >>> output = paddle.empty([2], dtype='int64') >>> dist.alltoall_single(output, data) >>> print(output) >>> # output for rank 0: [0, 2] >>> # output for rank 1: [1, 3] >>> # case 2 (2 GPUs) >>> in_split_sizes = [i + 1 for i in range(size)] >>> # in_split_sizes for rank 0: [1, 2] >>> # in_split_sizes for rank 1: [1, 2] >>> out_split_sizes = [rank + 1 for i in range(size)] >>> # out_split_sizes for rank 0: [1, 1] >>> # out_split_sizes for rank 1: [2, 2] >>> data = paddle.ones([sum(in_split_sizes), size], dtype='float32') * rank >>> # data for rank 0: [[0., 0.], [0., 0.], [0., 0.]] >>> # data for rank 1: [[1., 1.], [1., 1.], [1., 1.]] >>> output = paddle.empty([(rank + 1) * size, size], dtype='float32') >>> group = dist.new_group([0, 1]) >>> task = dist.alltoall_single(data, ... output, ... in_split_sizes, ... out_split_sizes, ... sync_op=False, ... group=group) >>> task.wait() >>> print(output) >>> # output for rank 0: [[0., 0.], [1., 1.]] >>> # output for rank 1: [[0., 0.], [0., 0.], [1., 1.], [1., 1.]] F)ralltoall_single) out_tensor in_tensorin_split_sizesout_split_sizesr rs rrr`s-P  ! !   r)NT) r list[Tensor]r rr Group | Nonerboolreturnr)NNNT)rrrrrlist[int] | Nonerrr rrrrr) __future__rtypingr paddle.distributed.communicationrpaddlerpaddle.base.corer&paddle.distributed.communication.grouprr rrrr#s# 3%<  C!C C C C  CR(,(, PPP%P& P  P  P Pr