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Specifications and Common Mistakes

- Specifications and Common Mistakes:

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

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mindspore.nn.TransformerDecoder

class mindspore.nn.TransformerDecoder(decoder_layer, num_layers, norm=None)

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. For unbatched input, the shape is $(T,E)$ ; otherwise if batch_first=False in TransformerDecoderLayer, the shape is $(T,N,E)$ and if batch_first=True , the shape is $(T,N,E)$, where $(T)$ is the target sequence length. Supported types: float16, float32, float64.

• memory (Tensor): The sequence from the last layer of the encoder. Supported types: float16, float32, float64.

• tgt_mask (Tensor, optional): the mask of the tgt sequence. The shape is $(T,T)$ or $(N\ast nhead,T,T)$ , where nhead is the arguent in TransformerDecoderLayer. Supported types: float16, float32, float64, bool. Default: None.

• memory_mask (Tensor, optional): the mask of the memory sequence. The shape is $(T,S)$ . Supported types: float16, float32, float64, bool. Default: None.

• tgt_key_padding_mask (Tensor, optional): the mask of the tgt keys per batch. Supported types: float16, float32, float64, bool. Default: None.

• memory_key_padding_mask (Tensor, optional): the mask of the memory keys per batch. The shape is $(S)$ for unbatched input, otherwise $(N,S)$ . Supported types: float16, float32, float64, bool. Default: None.

Outputs:

Tensor. The shape and dtype of Tensor is the same with tgt .

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore as ms
>>> import numpy as np
>>> decoder_layer = ms.nn.TransformerDecoderLayer(d_model=512, nhead=8)
>>> transformer_decoder = ms.nn.TransformerDecoder(decoder_layer, num_layers=6)
>>> memory = ms.Tensor(np.random.rand(10, 32, 512), ms.float32)
>>> tgt = ms.Tensor(np.random.rand(20, 32, 512), ms.float32)
>>> out = transformer_decoder(tgt, memory)
>>> print(out.shape)
(20, 32, 512)