pykernsformer
The pykernsformer module extends the torch.nn.TransformerEncoderLayer class to include custom attention formulas.
Installation
You can install the pykernsformer package using pip as
pip install pykernsformer
Usage
pykernsformer comes with the following built in attention kernels.
| pykernsformer | Attention | Formula | Citation |
|---|---|---|---|
attention |
Regular | $softmax(\frac{QK^T}{\sqrt{d_k}})V$ | Vaswani et al. |
attention_linear |
Linear | $\frac{QK^T}{\sum_k QK^T}V$ | |
attention_periodic |
Periodic | $softmax(-\frac{2\sin^2(\pi\frac{\sqrt{2 - 2q_ik_j^T}}{p})}{\sqrt{d_k}})V$ | |
attention_LP |
Locally Periodic | $softmax(-\frac{2\sin^2(\pi\frac{\sqrt{2 - 2\hat{q}_i\hat{k}_j^T}}{p})}{\sqrt{d_k}} + \frac{{q_i}{k_j^T}}{\sqrt{d_k}})V$ | |
attention_RQ |
Rational Quadratic | $\frac{\left( 1 + \frac{1}{\alpha \sqrt{d_k}} - \frac{2QK^T}{2 \alpha \sqrt{d_k}} \right)^{-\alpha}}{\sum_k \left( 1 + \frac{1}{\alpha \sqrt{d_k}} - \frac{2QK^T}{2 \alpha \sqrt{d_k}} \right)^{-\alpha}}V$ |
You can also implement your own attention function with the following signature:
def attention_custom(query, key, value, mask=None, dropout=None):
[...]
p_attn = [...] # the attention matrix
[...]
return torch.matmul(p_attn, value), p_attn
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Metadata
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Release files for pykernsformer 0.0.4
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