ΑiSSKJr SSKJrJr \(aSSKJr SSKJr SSK J r SSK J r SSK rSSKrSSKJr SSKJr SS KJr S S KJrJr SSS jjrSSS jjrg)) annotations) TYPE_CHECKINGAny)Sequence)Tensor)task)GroupN) framework)stream)convert_object_to_tensorconvert_tensor_to_objectc2[R"XX#U5$)a Scatter a tensor to all participators. As shown below, one process is started with a GPU and the source of the scatter is GPU0. Through scatter operator, the data in GPU0 will be sent to all GPUs averagely. .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/scatter.png :width: 800 :alt: scatter :align: center Args: tensor (Tensor): The output Tensor. Its data type should be float16, float32, float64, int32, int64, int8, uint8, bool or bfloat16. tensor_list (list|tuple): A list/tuple of Tensors to scatter. Every element in the list must be a Tensor whose data type should be float16, float32, float64, int32, int64, int8, uint8, bool or bfloat16. Default value is None. src (int): The source rank id. Default value is 0. group (Group, 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. The default value is True. Returns: None. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env: DISTRIBUTED) >>> import paddle >>> import paddle.distributed as dist >>> dist.init_parallel_env() >>> if dist.get_rank() == 0: ... data1 = paddle.to_tensor([7, 8, 9]) ... data2 = paddle.to_tensor([10, 11, 12]) ... dist.scatter(data1, src=1) >>> else: ... data1 = paddle.to_tensor([1, 2, 3]) ... data2 = paddle.to_tensor([4, 5, 6]) ... dist.scatter(data1, tensor_list=[data1, data2], src=1) >>> print(data1, data2) >>> # [1, 2, 3] [10, 11, 12] (2 GPUs, out for rank 0) >>> # [4, 5, 6] [4, 5, 6] (2 GPUs, out for rank 1) )r scatter)tensor tensor_listsrcgroupsync_ops h/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/distributed/communication/scatter.pyrr'sb >>&s7 CCc\[R"5(dS5e[R"5n/n/nXB:XaDUH2n[ U5upUR U5 UR U 5 M4 [ U5n O[R"/SS9n [R"X5 [U R55n /n UHQn U R5n[R"X/5n[R "U5nU R U5 MS [R"U /SS9n[#UXB:XaU OSX#5 [R"/SS9n[#UXB:XaUOSX#5 UR%5 UR ['UUR555 g)a Scatter picklable objects from the source to all others. Similar to scatter(), but python object can be passed in. Args: out_object_list (list): The list of objects to store the scattered objects. in_object_list (list): The list of objects to scatter. Only objects on the src rank will be scattered. src (int): The source rank in global view. group (Group): The group instance return by new_group or None for global default group. Returns: None. Warning: This API only supports the dygraph mode. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env: DISTRIBUTED) >>> import paddle.distributed as dist >>> dist.init_parallel_env() >>> out_object_list = [] # type: ignore >>> if dist.get_rank() == 0: ... in_object_list = [{'foo': [1, 2, 3]}, {'foo': [4, 5, 6]}] >>> else: ... in_object_list = [{'bar': [1, 2, 3]}, {'bar': [4, 5, 6]}] >>> dist.scatter_object_list(out_object_list, in_object_list, src=1) >>> print(out_object_list) >>> # [{'bar': [1, 2, 3]}] (2 GPUs, out for rank 0) >>> # [{'bar': [4, 5, 6]}] (2 GPUs, out for rank 1) z6scatter_object_list doesn't support static graph mode.int64)dtypeuint8N)r in_dynamic_modedistget_rankr appendmaxpaddleemptyr broadcastintitemnumpynpresize to_tensorrclearr)out_object_listin_object_listrrrankin_obj_tensors in_obj_sizesobj obj_tensorobj_sizemax_obj_size_tensor max_obj_sizein_tensor_listr numpy_data in_tensor out_tensorout_tensor_sizes rscatter_object_listr:[ssN  $ $ & &@ & ==?DNL {!C#;C#@ J  ! !* -    )"",/$ll2W= (.*//12LN \\^ YYz>: $$Z0 i( ! |nGrIs#%(%< !3,0 1D 1D(1D 1D  1D  1D  1Dl(, JJ$J J  J  Jr