mindspore.nn.TransformerDecoder
- class mindspore.nn.TransformerDecoder(decoder_layer, num_layers, norm=None)[source]
Transformer Decoder module with multi-layer stacked of TransformerDecoderLayer, including multihead self attention, cross attention and feedforward layer.
- Parameters
decoder_layer (Cell) – An instance of the
mindspore.nn.TransformerDecoderLayer
class.num_layers (int) – The number of decoder-layers in the decoder.
norm (Cell, optional) – The layer normalization module. Default: None.
- Inputs:
tgt (Tensor): The sequence to the decoder.
memory (Tensor): The sequence from the last layer of the encoder.
tgt_mask (Tensor, optional): the mask of the tgt sequence. Default:
None
.memory_mask (Tensor, optional): the mask of the memory sequence. Default:
None
.tgt_key_padding_mask (Tensor, optional): the mask of the tgt keys per batch. Default:
None
.memory_key_padding_mask (Tensor, optional): the mask of the memory keys per batch. Default:
None
.
- Outputs:
Tensor.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8) >>> transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6) >>> memory = Tensor(np.random.rand(10, 32, 512), mindspore.float32) >>> tgt = Tensor(np.random.rand(20, 32, 512), mindspore.float32) >>> out = transformer_decoder(tgt, memory) >>> print(out.shape) (20, 32, 512)