Document feedback

Question document fragment

When a question document fragment contains a formula, it is displayed as a space.

Submission type

issue

It's a little complicated...

I'd like to ask someone.

Please select the submission type

Problem type

Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

- Low writing fluency that does not affect understanding.

- Incorrect version numbers, including software package names and version numbers on the UI.

Usability

- Usability:

- Incorrect or missing key steps.

- Missing main function descriptions, keyword explanation, necessary prerequisites, or precautions.

- Ambiguous descriptions, unclear reference, or contradictory context.

- Unclear logic, such as missing classifications, items, and steps.

Correctness

- Correctness:

- Technical principles, function descriptions, supported platforms, parameter types, or exceptions inconsistent with that of software implementation.

- Incorrect schematic or architecture diagrams.

- Incorrect commands or command parameters.

- Incorrect code.

- Commands inconsistent with the functions.

- Wrong screenshots.

- Sample code running error, or running results inconsistent with the expectation.

Risk Warnings

- Risk Warnings:

- Lack of risk warnings for operations that may damage the system or important data.

Content Compliance

- Content Compliance:

- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

- Copyright infringement.

Please select the type of question

Problem description

Describe the bug so that we can quickly locate the problem.

Document feedback

mindspore.communication.comm_func.gather_into_tensor

mindspore.communication.comm_func.gather_into_tensor(tensor, dst=0, group=GlobalComm.WORLD_COMM_GROUP)[source]

Gathers tensors from the specified communication group. The operation will gather the tensor from processes according to dimension 0.

Note

Only the tensor in process dst (global rank) will keep the gathered tensor. The other process will keep a tensor with shape [1], which has no mathematical meaning. Only support PyNative mode, Graph mode is not currently supported.

Parameters

tensor (Tensor) – The tensor to be gathered. The shape of tensor is $(x_{1}, x_{2}, . . ., x_{R})$ .
dst (int, optional) – Specifies the rank(global rank) of the process that receive the tensor. And only process dst will receive the gathered tensor. Default: 0.
group (str, optional) – The communication group to work on. Default: GlobalComm.WORLD_COMM_GROUP.

Returns

Tensor, the shape of output is $(\sum x_{1}, x_{2}, . . ., x_{R})$ . The dimension 0 of data is equal to sum of the dimension of input tensor, and the other dimension keep the same.

Raises

TypeError – If the type of the first input parameter is not Tensor, or any of op and group is not a str.
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
>>>
>>> # Launch 2 processes.
>>>
>>> comm.init()
>>> input = ms.Tensor(np.arange(4).reshape([2, 2]).astype(np.float32))
>>> output = comm.comm_func.gather_into_tensor(tensor=input, dst=0)
>>> print(output)
Process with rank 0: [[0. 1.],
                      [2. 3.],
                      [0. 1.],
                      [2. 3.]]
Process with rank 1: [0]