1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
|
from typing import Optional
import torch
import torch.nn as nn
class SparseEmbedding(nn.Embedding):
def __init__(
self,
num_embeddings: int,
embedding_dim: int,
alpha: int = 1,
dropout: float = 0.0,
**kwargs
):
nn.Embedding.__init__(self, num_embeddings, embedding_dim, **kwargs)
self.register_buffer('trainable_ids', self.weight.new_zeros(num_embeddings, dtype=torch.long) - 1)
self.trainable = nn.ParameterList()
self.scaling = alpha
self.dropout_p = dropout
self.weight.requires_grad = False
if dropout > 0.:
self.dropout = nn.Dropout(p=dropout)
else:
self.dropout = nn.Identity()
self.reset_parameters()
def new_resized(self, new_num_embeddings: int, initializer_factor: Optional[float] = None):
n = min(self.num_embeddings, new_num_embeddings)
new_emb = SparseEmbedding(
new_num_embeddings,
self.embedding_dim,
self.scaling,
self.dropout_p,
device=self.weight.device,
dtype=self.weight.dtype
)
if initializer_factor is not None:
new_emb.weight.data.normal_(mean=0.0, std=initializer_factor * 0.02)
else:
nn.init.zeros_(new_emb.weight.data)
new_emb.weight.data[:n, :] = self.weight.data[:n, :]
for param in self.trainable:
new_emb.trainable.append(param)
new_emb.trainable_ids[:n] = self.trainable_ids[:n]
return new_emb
def mark_trainable(self, input_ids: torch.LongTensor):
trainable_ids = self.trainable_ids[input_ids]
new_ids = input_ids[trainable_ids == -1]
if new_ids.shape[0] == 0:
return
n1 = len(self.trainable)
n2 = n1 + new_ids.shape[0]
self.trainable_ids[new_ids] = torch.arange(n1, n2)
for _ in new_ids:
self.trainable.append(self.weight.new_zeros(self.embedding_dim))
def get_weights(self, input_ids: torch.Tensor):
original_shape = input_ids.shape
if len(input_ids.shape) != 1:
input_ids = input_ids.view(input_ids.shape[0] * input_ids.shape[1])
weights = self.weight.new_zeros((input_ids.shape[0], self.embedding_dim))
trainable_ids = self.trainable_ids[input_ids]
mask = ~(trainable_ids == -1)
elems = [self.trainable[id] for id in trainable_ids[mask]]
if len(elems) != 0:
w = self.dropout(torch.stack(elems)) * self.scaling
weights[mask] = w.to(dtype=weights.dtype)
if len(original_shape) != 1:
weights = weights.view(original_shape[0], original_shape[1], -1)
return weights
def persist(self):
self.weight.data += self.get_weights(torch.arange(self.trainable_ids.shape[0]))
self.trainable_ids[:] = -1
self.trainable = nn.ParameterList()
def reset_parameters(self):
nn.Embedding.reset_parameters(self)
if hasattr(self, "trainable"):
self.trainable_ids[:] = -1
self.trainable = nn.ParameterList()
def train(self, mode: bool = True):
nn.Embedding.train(self, mode)
self.trainable.train(mode)
def eval(self):
nn.Embedding.eval(self)
self.trainable.eval()
def forward(self, input_ids: torch.LongTensor):
result = nn.Embedding.forward(self, input_ids)
result += self.get_weights(input_ids)
return result
|
