Differences between torch.nn.TransformerEncoderLayer and mindspore.nn.TransformerEncoderLayer

torch.nn.TransformerEncoderLayer

class torch.nn.TransformerEncoderLayer(
    d_model,
    nhead,
    dim_feedforward=2048,
    dropout=0.1,
    activation='relu'
)(src, src_mask=None, src_key_padding_mask=None)

For more information, see torch.nn.TransformerEncoderLayer.

mindspore.nn.TransformerEncoderLayer

class mindspore.nn.TransformerEncoderLayer(
    d_model,
    nhead,
    dim_feedforward=2048,
    dropout=0.1,
    activation='relu',
    layer_norm_eps=1e-5,
    batch_first=False,
    norm_first=False,
    dtype=mstype.float32
)(src, src_mask=None, src_key_padding_mask=None)

For more information, see mindspore.nn.TransformerEncoderLayer.

Differences

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

Categories	Subcategories	PyTorch	MindSpore	Difference
Parameters	Parameter 1	d_model	d_model	Consistent function
	Parameter 2	nhead	nhead	Consistent function
	Parameter 3	dim_feedforward	dim_feedforward	Consistent function
	Parameter 4	dropout	dropout	Consistent function
	Parameter 5	activation	activation	Consistent function
	Parameter 6		layer_norm_eps	In MindSpore, the value of eps can be set in LayerNorm, PyTorch does not have this function
	Parameter 7		batch_first	In MindSpore, first batch can be set as batch dimension, PyTorch does not have this function
	Parameter 8		norm_first	In MindSpore, LayerNorm can be set in between Multiheadttention Layer and FeedForward Layer or after, PyTorch does not have this function
	Parameter 9		dtype	In MindSpore, dtype can be set in Parameters using ‘dtype’. PyTorch does not have this function.
Input	Input 1	src	src	Consistent function
	Input 2	src_mask	src_mask	In MindSpore, dtype can be set as float or bool Tensor; in PyTorch dtype can be set as float, byte or bool Tensor.
	Input 3	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)