Differences between torch.nn.Transformer and mindspore.nn.Transformer

torch.nn.Transformer

class torch.nn.Transformer(
    d_model=512,
    nhead=8,
    num_encoder_layers=6,
    num_decoder_layers=6,
    dim_feedforward=2048,
    dropout=0.1,
    activation='relu',
    custom_encoder=None,
    custom_decoder=None
)(src, tgt, src_mask=None, tgt_mask=None, memory_mask=None, src_key_padding_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None)

For more information, see torch.nn.Transformer.

mindspore.nn.Transformer

class mindspore.nn.Transformer(
    d_model=512,
    nhead=8,
    num_encoder_layers=6,
    num_decoder_layers=6,
    dim_feedforward=2048,
    dropout=0.1,
    activation='relu',
    custom_encoder=None,
    custom_decoder=None,
    layer_norm_eps=1e-05,
    batch_first=False,
    norm_first=False,
    dtype=mstype.float32
)(src, tgt, src_mask=None, tgt_mask=None, memory_mask=None, src_key_padding_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None)

For more information, see mindspore.nn.Transformer.

Differences

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

Categories	Subcategories	PyTorch	MindSpore	Difference
Parameters	Parameter 1	d_model	d_model	Consistent function
	Parameter 2	nhead	nhead	Consistent function
	Parameter 3	num_encoder_layers	num_encoder_layers	Consistent function
	Parameter 4	num_decoder_layers	num_decoder_layers	Consistent function
	Parameter 5	dim_feedforward	dim_feedforward	Consistent function
	Parameter 6	dropout	dropout	Consistent function
	Parameter 7	activation	activation	Consistent function
	Parameter 8	custom_encoder	custom_encoder	Consistent function
	Parameter 9	custom_decoder	custom_decoder	Consistent function
	Parameter 10		layer_norm_eps	In MindSpore, the value of eps can be set in LayerNorm, PyTorch does not have this function
	Parameter 11		batch_first	In MindSpore, first batch can be set as batch dimension, PyTorch does not have this function
	Parameter 12		norm_first	In MindSpore, LayerNorm can be set in between MultiheadAttention Layer and FeedForward Layer or after, PyTorch does not have this function
	Parameter 13		dtype	In MindSpore, dtype can be set for parameters using ‘dtype’. PyTorch does not have this function.
Input	Input 1	src	src	Consistent function
	Input 2	tgt	tgt	Consistent function
	Input 3	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 4	tgt_mask	tgt_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 5	memory_mask	memory_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 6	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.
	Input 7	tgt_key_padding_mask	tgt_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.
	Input 8	memory_key_padding_mask	memory_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

transformer_model = nn.Transformer(nhead=16, num_encoder_layers=12)
src = torch.rand(10, 32, 512)
tgt = torch.rand(10, 32, 512)
out = transformer_model(src, tgt)
print(out.shape)
#torch.Size([10, 32, 512])

# MindSpore
import mindspore as ms
import numpy as np

transformer_model = ms.nn.Transformer(nhead=16, num_encoder_layers=12)
src = ms.Tensor(np.random.rand(10, 32, 512), ms.float32)
tgt = ms.Tensor(np.random.rand(10, 32, 512), ms.float32)
out = transformer_model(src, tgt)
print(out.shape)
#(10, 32, 512)