mindspore.communication.comm_func.reduce_scatter_tensor

mindspore.communication.comm_func.reduce_scatter_tensor(tensor, op=ReduceOp.SUM, group=GlobalComm.WORLD_COMM_GROUP, async_op=False)[source]

Reduces and scatters tensors from the specified communication group and returns the tensor which is reduced and scattered.

Note

The tensors must have the same shape and format in all processes of the collection.

Parameters

  • tensor (Tensor) – The input tensor to be reduced and scattered, suppose it has a shape \((N, *)\), where * means any number of additional dimensions. N must be divisible by rank_size. rank_size refers to the number of cards in the communication group.

  • op (str, optional) – Specifies an operation used for element-wise reductions, like SUM and MAX. Default: ReduceOp.SUM .

  • group (str, optional) – The communication group to work on. Default: GlobalComm.WORLD_COMM_GROUP , which means "hccl_world_group" in Ascend, and "nccl_world_group" in GPU.

  • async_op (bool, optional) – Whether this operator should be an async operator. Default: False .

Returns

Tuple(Tensor, CommHandle), the output tensor has the same dtype as input_x with a shape of \((N/rank\_size, *)\). CommHandle is an async work handle, if async_op is set to True. CommHandle will be None, when async_op is False.

Raises

  • TypeError – If the type of the first input parameter is not Tensor, or any of op and group is not a str.

  • ValueError – If the first dimension of the input cannot be divided by the rank_size.

  • RuntimeError – If device target is invalid, or backend is invalid, or distributed initialization fails.

Supported Platforms:

Ascend

Examples

Note

Before running the following examples, you need to configure the communication environment variables.

For Ascend/GPU/CPU devices, it is recommended to use the msrun startup method without any third-party or configuration file dependencies. Please see the msrun start up for more details.

This example should be run with 2 devices.

>>> import numpy as np
>>> import mindspore as ms
>>> import mindspore.communication as comm
>>>
>>> comm.init()
>>> input_tensor = ms.Tensor(np.ones([8, 8]).astype(np.float32))
>>> output, _ = comm.comm_func.reduce_scatter_tensor(input_tensor)
>>> print(output)
[[2. 2. 2. 2. 2. 2. 2. 2.]
 [2. 2. 2. 2. 2. 2. 2. 2.]
 [2. 2. 2. 2. 2. 2. 2. 2.]
 [2. 2. 2. 2. 2. 2. 2. 2.]]