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Specifications and Common Mistakes

- Specifications and Common Mistakes:

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

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mindflow.cell.MultiHeadAttention

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

Multi Head Attention proposed in Attention Is All You Need.

Parameters

• in_channels (int) – The input channels.

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

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

• 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 $(batch\mathrm{\_}size,sequence\mathrm{\_}len,in\mathrm{\_}channels)$.

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

• key_padding_mask (Tensor) - Tensor with shape $(batch\mathrm{\_}size,sequence\mathrm{\_}len)$ or $(batch\mathrm{\_}size,sequence\mathrm{\_}len,sequence\mathrm{\_}len)$ 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 MultiHeadAttention
>>> model = MultiHeadAttention(in_channels=512, num_heads=4)
>>> x = ops.rand((2, 32, 512))
>>> mask_shape = (2, 4, 32, 32)
>>> mask = ops.ones(mask_shape)
>>> output = model(x, mask)
>>> print(output.shape)
(2, 32, 512)