File size: 7,678 Bytes
af87020 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 | import math
from typing import Optional, List
from functools import lru_cache
import unittest
import torch
import torch.nn.functional as F
n_dists = {
0: [1],
1: [0.4, 0.6],
2: [0.2, 0.3, 0.5],
3: [0.1, 0.2, 0.3, 0.4],
4: [0.1, 0.15, 0.2, 0.25, 0.3],
}
strats = {"linear": lambda x: x, "log": lambda x: math.log(x + 1), "exp": lambda x: x**2}
@lru_cache(maxsize=5)
def soft_dist(n):
return [1 / n] * n
@lru_cache(maxsize=5)
def n_dist(n: int, strategy: str) -> list[float]:
"""dist of ngram weight is logarithmic"""
ns = list(range(1, n + 1))
xs = list(map(strats[strategy], ns))
result = list(map(lambda x: x / sum(xs), xs))
return result
def soft_n_hot(
input,
num_classes: int,
strategy: Optional[str],
):
shape = list(input.size())[1:]
shape.append(num_classes)
ret = torch.zeros(shape).to(input.device)
if strategy:
soft_labels = n_dist(input.size(0), strategy)
else:
soft_labels = [1] * input.size(0)
for i, t in enumerate(input):
ret.scatter_(-1, t.unsqueeze(-1), soft_labels[i])
return ret
def n_hot(t, num_clases, ngram_sequences: Optional[torch.Tensor] = None, unk_idx: Optional[int] = None):
shape = list(t.size())
if ngram_sequences is not None:
shape.append(num_clases)
ret = torch.zeros(shape).to(t.device)
ret.scatter_(-1, t.unsqueeze(-1), 1)
for seq in ngram_sequences:
if unk_idx is not None:
mask = torch.eq(seq, unk_idx)
seq[mask] = t[mask]
ret.scatter_(-1, seq.unsqueeze(-1), 1)
return ret
elif len(shape) == 2:
return F.one_hot(t, num_classes=num_clases).float()
else:
shape = shape[1:]
shape.append(num_clases)
ret = torch.zeros(shape).to(t.device)
# Expect that first dimension is for all n-grams
for seq in t:
ret.scatter_(-1, seq.unsqueeze(-1), 1)
return ret
class NGramsEmbedding(torch.nn.Embedding):
"""N-Hot encoder"""
def __init__(
self,
num_embeddings: int,
embedding_dim: int,
padding_idx: Optional[int] = None,
max_norm: Optional[float] = None,
norm_type: float = 2,
scale_grad_by_freq: bool = False,
sparse: bool = False,
_weight: Optional[torch.Tensor] = None,
device=None,
dtype=None,
unk_idx: Optional[int] = None
) -> None:
super().__init__(
num_embeddings,
embedding_dim,
padding_idx=padding_idx,
max_norm=max_norm,
norm_type=norm_type,
scale_grad_by_freq=scale_grad_by_freq,
sparse=sparse,
_weight=_weight,
device=device,
dtype=dtype,
)
self.num_classes = num_embeddings
self.unk_idx = unk_idx
def forward(self, input: torch.Tensor, ngram_sequences: Optional[torch.Tensor] = None):
return self._forward(
n_hot(input, self.num_classes, ngram_sequences, self.unk_idx)
)
def _forward(self, n_hot: torch.Tensor) -> torch.Tensor:
return F.linear(n_hot, self.weight.t())
def collect_n_gram_sequences(**kwargs) -> List[torch.Tensor]:
sequences = []
for n in range(2, len(kwargs)+2):
s = kwargs[f"gram_{n}_sequence"]
if s is not None:
sequences.append(s)
else:
break
return sequences
def shift_with_pad(target_tensor, n, from_tensor):
shifted = target_tensor[:, n:]
seq_size = target_tensor.size(1) - 1
missing_idxs = torch.arange(seq_size - (n-1), seq_size).to(target_tensor.device)
# Pad with missing idxs from unigram tensor
shifted = torch.concat(
(shifted, from_tensor.index_select(1, missing_idxs)), dim=1
)
return shifted
class TestNGME(unittest.TestCase):
def test_one_hot(self):
t = torch.tensor([[0, 1, 2]])
ret = n_hot(t, 3)
expected = torch.eye(3)
assert torch.all(torch.eq(ret, expected))
def test_multi_hot1(self):
t = torch.tensor([[0, 1, 2]])
# [batch, ngram, seq]
two_grams = torch.tensor([[[0, 1, 2]]])
ret = n_hot(t, 3, two_grams)
expected = torch.eye(3)
assert torch.all(torch.eq(ret, expected))
def test_multi_hot2(self):
t = torch.tensor([[0, 1, 2]])
two_three_grams = torch.tensor([[[1, 2, 0]], [[2, 0, 1]]])
ret = n_hot(t, 3, two_three_grams)
expected = torch.ones(3, 3)
assert torch.all(torch.eq(ret, expected))
class TestShifting(unittest.TestCase):
def test_two_gram(self):
two_gram_batch = torch.tensor([[0, 1, 2, 3, 4]])
from_tensor = torch.tensor([[-4, -3, -2, -1]])
shifted = _shift_with_pad(two_gram_batch, 2, from_tensor)
expected = torch.tensor([[2, 3, 4, -1]])
assert torch.all(torch.eq(shifted, expected))
def test_three_gram(self):
three_gram_batch = torch.tensor([[0, 1, 2, 3, 4, 5, 6]])
from_tensor = torch.tensor([[-6, -5, -4, -3, -2, -1]])
shifted = _shift_with_pad(three_gram_batch, 3, from_tensor)
expected = torch.tensor([[3, 4, 5, 6, -2, -1]])
assert torch.all(torch.eq(shifted, expected))
def test_three_gram_2(self):
three_gram_batch = torch.tensor([[0, 1, 2, 3, 4, 5, 6], [0, 1, 2, 3, 4, 5, 6]])
from_tensor = torch.tensor([[-6, -5, -4, -3, -2, -1], [-6, -5, -4, -3, -2, -1]])
shifted = _shift_with_pad(three_gram_batch, 3, from_tensor)
expected = torch.tensor([[3, 4, 5, 6, -2, -1], [3, 4, 5, 6, -2, -1]])
assert torch.all(torch.eq(shifted, expected))
class TestNGramEmbeddings(unittest.TestCase):
def test(self):
emb = NGramsEmbedding(10, 10)
emb.weight = torch.nn.Parameter(torch.eye(10))
emb1 = emb(torch.tensor([[1, 2, 3]]))
emb2 = emb(torch.tensor([[4, 5, 6]]))
emb3 = emb(torch.tensor([[1, 2, 3]]), [torch.tensor([[4, 5, 6]])])
assert torch.all(torch.eq(torch.add(emb1, emb2), emb3))
def test_2(self):
emb = NGramsEmbedding(10, 10)
emb.weight = torch.nn.Parameter(torch.eye(10))
emb1 = emb(torch.tensor([[1, 2, 3]]))
emb2 = emb(torch.tensor([[1, 2, 3]]))
emb3 = emb(torch.tensor([[1, 2, 3]]), [torch.tensor([[1, 2, 3]])])
assert torch.all(torch.eq(emb1, emb3))
assert torch.all(torch.eq(emb2, emb3))
def test_3_gram(self):
emb = NGramsEmbedding(10, 10)
emb.weight = torch.nn.Parameter(torch.eye(10))
emb1 = emb(torch.tensor([[1, 2, 3]]))
emb2 = emb(torch.tensor([[4, 5, 6]]))
emb3 = emb(torch.tensor([[7, 8, 9]]))
emb4 = emb(torch.tensor([[1, 2, 3]]), [torch.tensor([[4, 5, 6]]), torch.tensor([[7, 8, 9]])])
assert torch.all(torch.eq(torch.add(torch.add(emb1, emb2), emb3), emb4))
def test_ignore_indx(self):
emb = NGramsEmbedding(10, 10, unk_idx=0)
emb.weight = torch.nn.Parameter(torch.eye(10))
unigram = torch.tensor([[1, 2, 3]])
bigram = torch.tensor([[0, 0, 0]])
emb1 = emb(unigram, [bigram])
emb2 = emb(unigram)
assert torch.all(torch.eq(emb1, emb2))
def test_ignore_indx_2(self):
emb = NGramsEmbedding(10, 10, unk_idx=0)
emb.weight = torch.nn.Parameter(torch.eye(10))
unigram = torch.tensor([[0, 2, 3]])
bigram = torch.tensor([[0, 0, 0]])
emb1 = emb(unigram, [bigram])
emb2 = emb(unigram)
assert torch.all(torch.eq(emb1, emb2))
if __name__ == '__main__':
unittest.main()
|