比较与torch.nn.TransformerDecoder的差异

torch.nn.TransformerDecoder

class torch.nn.TransformerDecoder(
    decoder_layer,
    num_layers,
    norm=None
)(tgt, memory, tgt_mask=None, memory_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None)

更多内容详见torch.nn.TransformerDecoder。

mindspore.nn.TransformerDecoder

class mindspore.nn.TransformerDecoder(
    decoder_layer,
    num_layers,
    norm=None
)(tgt, memory, tgt_mask=None, memory_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None)

更多内容详见mindspore.nn.TransformerDecoder。

差异对比

torch.nn.TransformerDecoder 和 mindspore.nn.TransformerDecoder 用法基本一致。

分类	子类	PyTorch	MindSpore	差异
参数	参数1	decoder_layer	decoder_layer	功能一致
	参数2	num_layers	num_layers	功能一致
	参数3	norm	norm	功能一致
输入	输入1	tgt	tgt	功能一致
	输入2	memory	memory	功能一致
	输入3	tgt_mask	tgt_mask	MindSpore中dtype可设置为float或bool Tensor，PyTorch中dtype可设置为float、byte或bool Tensor
	输入4	memory_mask	memory_mask	MindSpore中dtype可设置为float或bool Tensor，PyTorch中dtype可设置为float、byte或bool Tensor
	输入5	tgt_key_padding_mask	tgt_key_padding_mask	MindSpore中dtype可设置为float或bool Tensor，PyTorch中dtype可设置为byte或bool Tensor
	输入6	memory_key_padding_mask	memory_key_padding_mask	MindSpore中dtype可设置为float或bool Tensor，PyTorch中dtype可设置为byte或bool Tensor

代码示例

# PyTorch
import torch
from torch import nn

decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8)
transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6)
memory = torch.rand(10, 32, 512)
tgt = torch.rand(20, 32, 512)
out = transformer_decoder(tgt, memory)
print(out.shape)
#torch.Size([20, 32, 512])

# MindSpore
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)