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

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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 usage of mindspore.nn.TransformerEncoderLayer is mostly the same with that of 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)