Differences between torch.nn.TransformerEncoder and mindspore.nn.TransformerEncoder

torch.nn.TransformerEncoder

class torch.nn.TransformerEncoder(
    encoder_layer,
    num_layers,
    norm=None
)(src, mask=None, src_key_padding_mask=None)

For more information, see torch.nn.TransformerEncoder.

mindspore.nn.TransformerEncoder

class mindspore.nn.TransformerEncoder(
    encoder_layer,
    num_layers,
    norm=None
)(src, src_mask=None, src_key_padding_mask=None)

For more information, see mindspore.nn.TransformerEncoder.

Differences

The code implementation and parameter update logic of mindspore.nn.TransformerEncoder optimizer is mostly the same with torch.nn.TransformerEncoder.

Categories	Subcategories	PyTorch	MindSpore	Difference
Parameters	Parameter 1	encoder_layer	encoder_layer	Consistent function
	Parameter 2	num_layers	num_layers	Consistent function
	Parameter 3	norm	norm	Consistent function
Input	Input1	src	src	Consistent function
	Input2	mask	src_mask	Consistent function, different parameter names
	Input3	src_key_padding_mask	src_key_padding_mask	In MindSpore, dtype can be set as float or bool Tensor; in PyTorch dtype can be set as byte or bool Tensor.

Code Example

# PyTorch
import torch
from torch import nn

encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=6)
src = torch.rand(10, 32, 512)
out = transformer_encoder(src)
print(out.shape)
#torch.Size([10, 32, 512])

# MindSpore
import mindspore
from mindspore import nn

encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=6)
src = mindspore.numpy.rand(10, 32, 512)
out = transformer_encoder(src)
print(out.shape)
#(10, 32, 512)