mindspore.nn.TransformerEncoderLayer

class mindspore.nn.TransformerEncoderLayer(d_model: int, nhead: int, dim_feedforward: int = 2048, dropout: float = 0.1, activation: Union[str, Cell, callable] = 'relu', layer_norm_eps: float = 1e-05, batch_first: bool = False, norm_first: bool = False)[源代码]

Transformer的编码器层。Transformer编码器的单层实现,包括MultiheadAttention层和FeedForward层。

参数:
  • d_model (int) - 输入的特征数。

  • nhead (int) - 注意力头的数量。

  • dim_feedforward (int) - FeedForward层的维数。默认值:2048

  • dropout (float) - 随机丢弃比例。默认值:0.1

  • activation (Union[str, callable, Cell]) - 中间层的激活函数,可以输入字符串("relu""gelu")、函数接口(ops.reluops.gelu)或激活函数层实例(nn.ReLU()nn.GELU())。默认值:"relu"

  • layer_norm_eps (float) - LayerNorm层的eps值,默认值:1e-5

  • batch_first (bool) - 如果为 True 则输入输出Shape为 \((batch, seq, feature)\) ,反之,Shape为 \((seq, batch, feature)\) 。默认值: False

  • norm_first (bool) - 如果为 True, 则LayerNorm层位于MultiheadAttention层和FeedForward层之前,反之,位于其后。默认值: False

输入:
  • src (Tensor) - 源序列。

  • src_mask (Tensor, 可选) - 源序列的掩码矩阵。默认值:None

  • src_key_padding_mask (Tensor, 可选) - 源序列Key矩阵的掩码矩阵。默认值:None

输出:

Tensor。

支持平台:

Ascend GPU CPU

样例:

>>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
>>> src = Tensor(np.random.rand(10, 32, 512), mindspore.float32)
>>> out = encoder_layer(src)
>>> # Alternatively, when batch_first=True:
>>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8, batch_first=True)
>>> src = Tensor(np.random.rand(32, 10, 512), mindspore.float32)
>>> out = encoder_layer(src)
>>> print(out.shape)
(32, 10, 512)