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import torch
import torch.nn as nn
class UstaCausalAttention(nn.Module):
def __init__(self, embedding_dim, output_dim, context_length, dropout_rate = 0):
super().__init__()
self.embedding_dim = embedding_dim
self.q_weights = nn.Linear(embedding_dim, output_dim, bias=False)
self.k_weights = nn.Linear(embedding_dim, output_dim, bias=False)
self.v_weights = nn.Linear(embedding_dim, output_dim, bias=False)
self.dropout = nn.Dropout(dropout_rate)
self.register_buffer("mask", torch.tril(torch.ones(context_length, context_length)))
self.context_length = context_length
def forward(self, x):
number_of_tokens = x.shape[0]
# truncate the context length to the context length of the model
x = x[:self.context_length]
q = self.q_weights(x)
k = self.k_weights(x)
v = self.v_weights(x)
attention_scores = q @ k.T
attention_scores = attention_scores.masked_fill_(
self.mask.bool()[:number_of_tokens, :number_of_tokens] == 0, -torch.inf
)
attention_scores = torch.softmax(attention_scores / k.shape[-1] ** 0.5, dim=1)
attention_scores = self.dropout(attention_scores)
return attention_scores @ v
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