mindspore.ops.ReduceScatter

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class mindspore.ops.ReduceScatter(op=ReduceOp.SUM, group=GlobalComm.WORLD_COMM_GROUP)[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

  • 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 .

Inputs:

  • input_x (Tensor) - Input Tensor, 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.

Outputs:

Tensor, it has the same dtype as input_x with a shape of \((N/rank\_size, *)\).

Raises

  • TypeError – If any of operation and group is not a string.

  • 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 GPU

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 mindspore as ms
>>> from mindspore import Tensor
>>> from mindspore.communication import init
>>> from mindspore.ops import ReduceOp
>>> import mindspore.nn as nn
>>> from mindspore import ops
>>> import numpy as np
>>>
>>> ms.set_context(mode=ms.GRAPH_MODE)
>>> init()
>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.reducescatter = ops.ReduceScatter(ReduceOp.SUM)
...
...     def construct(self, x):
...         return self.reducescatter(x)
...
>>> input_ = Tensor(np.ones([8, 8]).astype(np.float32))
>>> net = Net()
>>> output = net(input_)
>>> 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.]]
Tutorial Examples: