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import torch.nn as nn
from .usta_multi_head_attention import UstaMultiHeadAttention
from .usta_layer_norm import UstaLayerNorm
from .usta_mlp import UstaMLP
class UstaDecoderBlock(nn.Module):
def __init__(self, embedding_dim, num_heads, context_length):
super().__init__()
self.self_attention = UstaMultiHeadAttention(embedding_dim, embedding_dim, context_length, num_heads, dropout_rate=0.5)
self.norm1 = UstaLayerNorm(embedding_dim)
self.mlp = UstaMLP(embedding_dim, embedding_dim)
self.norm2 = UstaLayerNorm(embedding_dim)
def forward(self, x):
res = self.norm1(x)
x = self.self_attention(x)
x = self.norm1(x)
x = x + res
res = x
res = self.norm2(x)
x = self.norm2(x)
x = x + res
return x
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