/
positional_encodings.py
173 lines (160 loc) · 5.46 KB
/
positional_encodings.py
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import math
import unittest
import torch
class SinusoidEncoding(torch.nn.Module):
"""
Mostly copied from
https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial6/Transformers_and_MHAttention.html
"""
def __init__(self, hidden_dim, max_len=5000):
"""
Inputs
d_model - Hidden dimensionality of the input.
max_len - Maximum length of a sequence to expect.
"""
super().__init__()
# Create matrix of [SeqLen, HiddenDim] representing the positional encoding for max_len inputs
pos_embed = torch.zeros(max_len, hidden_dim)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, hidden_dim, 2).float() * (-math.log(10000.0) / hidden_dim)
)
pos_embed[:, 0::2] = torch.sin(position * div_term)
pos_embed[:, 1::2] = torch.cos(position * div_term)
pos_embed = pos_embed.unsqueeze(0)
# register_buffer => Tensor which is not a parameter, but should be part of the modules state.
# Used for tensors that need to be on the same device as the module.
# persistent=False tells PyTorch to not add the buffer to the state dict (e.g. when we save the model)
self.register_buffer("pos_embed", pos_embed, persistent=False)
def forward(self, x):
"""
Adds positional embeddings to token embeddings.
N = batch size
L = sequence length
E = embedding dim
:param x: token embeddings. Shape: (N, L, E)
:return: token_embeddings + positional embeddings. Shape: (N, L, E)
"""
x = x + self.pos_embed[:, : x.size(1)]
return x
class TestSinusoidEncoding(unittest.TestCase):
def test_create_embedding(self):
batch = 1
dim = 8
len = 3
x = torch.zeros(batch, len, dim)
encoding = SinusoidEncoding(dim).forward(x)
expected = torch.Tensor(
[
[
[
0.0000e00,
1.0000e00,
0.0000e00,
1.0000e00,
0.0000e00,
1.0000e00,
0.0000e00,
1.0000e00,
],
[
8.4147e-01,
5.4030e-01,
9.9833e-02,
9.9500e-01,
9.9998e-03,
9.9995e-01,
1.0000e-03,
1.0000e00,
],
[
9.0930e-01,
-4.1615e-01,
1.9867e-01,
9.8007e-01,
1.9999e-02,
9.9980e-01,
2.0000e-03,
1.0000e00,
],
]
]
)
torch.testing.assert_close(encoding, expected, rtol=10e-5, atol=10e-5)
def test_create_embedding_multi_batch(self):
batch = 2
dim = 8
len = 3
x = torch.zeros(batch, len, dim)
encoding = SinusoidEncoding(dim).forward(x)
expected = torch.Tensor(
[
[
[
0.0000e00,
1.0000e00,
0.0000e00,
1.0000e00,
0.0000e00,
1.0000e00,
0.0000e00,
1.0000e00,
],
[
8.4147e-01,
5.4030e-01,
9.9833e-02,
9.9500e-01,
9.9998e-03,
9.9995e-01,
1.0000e-03,
1.0000e00,
],
[
9.0930e-01,
-4.1615e-01,
1.9867e-01,
9.8007e-01,
1.9999e-02,
9.9980e-01,
2.0000e-03,
1.0000e00,
],
],
[
[
0.0000e00,
1.0000e00,
0.0000e00,
1.0000e00,
0.0000e00,
1.0000e00,
0.0000e00,
1.0000e00,
],
[
8.4147e-01,
5.4030e-01,
9.9833e-02,
9.9500e-01,
9.9998e-03,
9.9995e-01,
1.0000e-03,
1.0000e00,
],
[
9.0930e-01,
-4.1615e-01,
1.9867e-01,
9.8007e-01,
1.9999e-02,
9.9980e-01,
2.0000e-03,
1.0000e00,
],
],
]
)
torch.testing.assert_close(encoding, expected, rtol=10e-5, atol=10e-5)
if __name__ == "__main__":
unittest.main()