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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

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- 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

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- 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.

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mindflow.cell.AttentionBlock

class mindflow.cell.AttentionBlock(in_channels, num_heads, drop_mode='dropout', dropout_rate=0.0, compute_dtype=mstype.float32)

AttentionBlock comprises an MultiHeadAttention and an MLP layer.

Parameters

• in_channels (int) – The input channels.

• num_heads (int) – The number of attention heads.

• drop_mode (str) – Dropout method. Default: dropout. Support dropout or droppath.

• dropout_rate (float) – The drop rate of dropout layer, greater than 0 and less equal than 1. Default: 0.0.

• compute_dtype (mindspore.dtype) – Compute dtype. Default: mstype.float32, indicates mindspore.float32.

Inputs:

• x (Tensor) - Tensor with shape $(batch\mathrm{\_}size,sequence\mathrm{\_}len,in\mathrm{\_}channels)$.

• mask (Tensor) - Tensor with shape $(batch\mathrm{\_}size,sequence\mathrm{\_}len,sequence\mathrm{\_}len)$ or $(sequence\mathrm{\_}len,sequence\mathrm{\_}len)$ or $(batch\mathrm{\_}size,nu{m}_{h}eads,sequence\mathrm{\_}len,sequence\mathrm{\_}len)$.

Outputs:

• output (Tensor) - Tensor with shape $(batch\mathrm{\_}size,sequence\mathrm{\_}len,in\mathrm{\_}channels)$.

Supported Platforms:

Ascend CPU

Examples

>>> from mindspore import ops
>>> from mindflow.cell import AttentionBlock
>>> model = AttentionBlock(in_channels=256, num_heads=4)
>>> x = ops.rand((4, 100, 256))
>>> output = model(x)
>>> print(output.shape)
(4, 100, 256)