Differences between torch.nn.TransformerDecoderLayer and mindspore.nn.TransformerDecoderLayer

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torch.nn.TransformerDecoderLayer

class torch.nn.TransformerDecoderLayer(
    d_model,
    nhead,
    dim_feedforward=2048,
    dropout=0.1,
    activation='relu'
)(tgt, memory, tgt_mask=None, memory_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None)

For more information, see torch.nn.TransformerDecoderLayer.

mindspore.nn.TransformerDecoderLayer

class mindspore.nn.TransformerDecoderLayer(
    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
)(tgt, memory, tgt_mask=None, memory_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None)

For more information, see mindspore.nn.TransformerDecoderLayer.

Differences

The usage of mindspore.nn.TransformerDecoderLayer is mostly the same with that of torch.nn.TransformerDecoderLayer.

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

tgt

tgt

Consistent function

Input 2

memory

memory

Consistent function

Input 3

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 4

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 5

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 6

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

decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8)
transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6)
memory = torch.rand(10, 32, 512)
tgt = torch.rand(20, 32, 512)
out = transformer_decoder(tgt, memory)
print(out.shape)
#torch.Size([20, 32, 512])

# MindSpore
import mindspore as ms
import numpy as np

decoder_layer = ms.nn.TransformerDecoderLayer(d_model=512, nhead=8)
transformer_decoder = ms.nn.TransformerDecoder(decoder_layer, num_layers=6)
memory = ms.Tensor(np.random.rand(10, 32, 512), ms.float32)
tgt = ms.Tensor(np.random.rand(20, 32, 512), ms.float32)
out = transformer_decoder(tgt, memory)
print(out.shape)
#(20, 32, 512)