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)