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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

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

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Problem description

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

mindspore.ops.bmm

View Source On Gitee
mindspore.ops.bmm(input_x, mat2)[source]

Perform a batch matrix-matrix product of matrices in input tensors.

output[...,:,:]=matrix(inputx[...,:,:])matrix(mat2[...,:,:])

The dim of input_x can not be less than 3 and the dim of mat2 can not be less than 2.

Parameters
  • input_x (Tensor) – The first tensor to be multiplied.

  • mat2 (Tensor) – The second tensor to be multiplied.

Returns

Tensor

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> input_x = mindspore.ops.arange(24, dtype=mindspore.float32).reshape(2, 4, 1, 3)
>>> mat2 = mindspore.ops.arange(72, dtype=mindspore.float32).reshape(2, 4, 3, 3)
>>> out = mindspore.ops.bmm(input_x, mat2)
>>> print(out)
[[[[  15,   18,   21]],
 [[ 150,  162,  174]],
 [[ 447,  468,  489]],
 [[ 906,  936,  966]]],
 [[[1527, 1566, 1605]],
 [[2310, 2358, 2406]],
 [[3255, 3312, 3369]],
 [[4362, 4428, 4494]]]]