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"""Transformer decoder module."""
from copy import deepcopy
from typing import Optional, Type
from torch import Tensor, nn
from text_recognizer.networks.transformer.attention import Attention
from text_recognizer.networks.transformer.embeddings.rotary import RotaryEmbedding
from text_recognizer.networks.transformer.ff import FeedForward
class DecoderBlock(nn.Module):
"""Residual decoder block."""
def __init__(
self,
self_attn: Attention,
norm: Type[nn.Module],
ff: FeedForward,
cross_attn: Optional[Attention] = None,
) -> None:
super().__init__()
self.ln_attn = norm
self.attn = self_attn
self.ln_cross_attn = deepcopy(norm)
self.cross_attn = cross_attn
self.ln_ff = deepcopy(norm)
self.ff = ff
def forward(
self,
x: Tensor,
rotary_embedding: RotaryEmbedding,
context: Optional[Tensor] = None,
mask: Optional[Tensor] = None,
) -> Tensor:
"""Applies decoder block on input signals."""
x = x + self.attn(self.ln_attn(x), mask=mask, rotary_embedding=rotary_embedding)
x = x + self.cross_attn(
x=self.ln_cross_attn(x),
context=context,
)
x = x + self.ff(self.ln_ff(x))
return x
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