Differences between torch.nn.MultiheadAttention and mindspore.nn.MultiheadAttention
torch.nn.MultiheadAttention
class torch.nn.MultiheadAttention(
embed_dim,
num_heads,
dropout=0.0,
bias=True,
add_bias_kv=False,
add_zero_attn=False,
kdim=None,
vdim=None
)(query, key, value, key_padding_mask=None, need_weights=True, attn_mask=None)
For more information, see torch.nn.MultiheadAttention。
mindspore.nn.MultiheadAttention
class mindspore.nn.MultiheadAttention(
embed_dim,
num_heads,
dropout=0.0,
has_bias=True,
add_bias_kv=False,
add_zero_attn=False,
kdim=None,
vdim=None,
batch_first=False,
dtype=mstype.float32
)(query, key, value, key_padding_mask=None, need_weights=True, attn_mask=None, average_attn_weights=True)
For more information, see mindspore.nn.MultiheadAttention。
Differences
The code implementation and parameter update logic of mindspore.nn.MultiheadAttention
optimizer is mostly the same with torch.nn.MultiheadAttention
.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameters |
Parameter1 |
embed_dim |
embed_dim |
Consistent function |
Parameter2 |
num_heads |
num_heads |
Consistent function |
|
Parameter3 |
dropout |
dropout |
Consistent function |
|
Parameter4 |
bias |
has_bias |
Consistent function |
|
Parameter5 |
add_bias_kv |
add_bias_kv |
Consistent function |
|
Parameter6 |
add_zero_attn |
add_zero_attn |
Consistent function |
|
Parameter7 |
kdim |
kdim |
Consistent function |
|
Parameter8 |
vdim |
vdim |
Consistent function |
|
Parameter9 |
batch_first |
In MindSpore, first batch can be set as batch dimension, PyTorch does not have this function. |
||
Parameter10 |
dtype |
In MindSpore, dtype can be set in Parameters using 'dtype'. PyTorch does not have this function. |
||
Input |
Input1 |
query |
query |
Consistent function |
Input2 |
key |
key |
Consistent function |
|
Input3 |
value |
value |
Consistent function |
|
Input4 |
key_padding_mask |
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. |
|
Input5 |
need_weights |
need_weights |
Consistent function |
|
Input6 |
attn_mask |
attn_mask |
In MindSpore, dtype can be set as float or bool Tensor; in PyTorch dtype can be set as float, byte or bool Tensor. |
|
Input7 |
average_attn_weights |
If true, indicates that the returned attn_weights should be averaged across heads. Otherwise, attn_weights are provided separately per head. PyTorch does not have this function. |
Code Example
# PyTorch
import torch
from torch import nn
embed_dim, num_heads = 128, 8
seq_length, batch_size = 10, 8
query = torch.rand(seq_length, batch_size, embed_dim)
key = torch.rand(seq_length, batch_size, embed_dim)
value = torch.rand(seq_length, batch_size, embed_dim)
multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
attn_output, attn_output_weights = multihead_attn(query, key, value)
print(attn_output.shape)
#torch.Size([10, 8, 128])
print(attn_output_weights.shape)
#torch.Size([8, 10, 10])
# MindSpore
import mindspore as ms
import numpy as np
embed_dim, num_heads = 128, 8
seq_length, batch_size = 10, 8
query = ms.Tensor(np.random.randn(seq_length, batch_size, embed_dim), ms.float32)
key = ms.Tensor(np.random.randn(seq_length, batch_size, embed_dim), ms.float32)
value = ms.Tensor(np.random.randn(seq_length, batch_size, embed_dim), ms.float32)
multihead_attn = ms.nn.MultiheadAttention(embed_dim, num_heads)
attn_output, attn_output_weights = multihead_attn(query, key, value)
print(attn_output.shape)
#(10, 8, 128)
print(attn_output_weights.shape)
#(8, 10, 10)