File size: 1,120 Bytes
3c62626 | 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 | import torch.nn as nn
import torch
class CausalSelfAttention(nn.Module):
def __init__(self, embed_dim):
super().__init__()
self.embed_dim = embed_dim
# Query | Key | Value
self.query = nn.Linear(
embed_dim,
embed_dim
)
self.key = nn.Linear(
embed_dim,
embed_dim
)
self.value = nn.Linear(
embed_dim,
embed_dim
)
self.out = nn.Linear(embed_dim, embed_dim)
def forward(self, x):
batch_size, seq_len, embed_dim = x.shape
Q = self.query(x)
K = self.key(x)
V = self.value(x)
scores = Q @ K.transpose(-2, -1)
scores = scores / (embed_dim ** 0.5)
# Causal Mask
mask = torch.triu(
torch.ones(seq_len, seq_len, device=x.device),
diagonal=1
).bool()
scores = scores.masked_fill(mask, float("-inf"))
attention_weight = torch.softmax(scores, dim=-1)
output = attention_weight @ V
output = self.out(output)
return output
|