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"""Implementes the attention module for the transformer."""
from typing import Optional, Tuple
import torch
import torch.nn.functional as F
from einops import rearrange
from torch import Tensor, einsum, nn
from text_recognizer.networks.transformer.embeddings.rotary import (
RotaryEmbedding,
rotate_half,
)
class Attention(nn.Module):
"""Standard attention."""
def __init__(
self,
dim: int,
num_heads: int,
causal: bool = False,
dim_head: int = 64,
dropout_rate: float = 0.0,
rotary_embedding: Optional[RotaryEmbedding] = None,
) -> None:
super().__init__()
self.dim = dim
self.num_heads = num_heads
self.causal = causal
self.dim_head = dim_head
self.dropout_rate = dropout_rate
self.rotary_embedding = rotary_embedding
self.scale = self.dim**-0.5
inner_dim = self.num_heads * self.dim_head
self.to_q = nn.Linear(self.dim, inner_dim, bias=False)
self.to_k = nn.Linear(self.dim, inner_dim, bias=False)
self.to_v = nn.Linear(self.dim, inner_dim, bias=False)
self.dropout = nn.Dropout(p=self.dropout_rate)
# Feedforward
self.fc = nn.Linear(inner_dim, self.dim)
def forward(
self,
x: Tensor,
context: Optional[Tensor] = None,
input_mask: Optional[Tensor] = None,
context_mask: Optional[Tensor] = None,
) -> Tensor:
"""Computes the attention."""
b, n, _, device = *x.shape, x.device
q = self.to_q(x)
k = self.to_k(context) if context is not None else self.to_k(x)
v = self.to_v(context) if context is not None else self.to_v(x)
q, k, v = map(
lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.num_heads), (q, k, v)
)
if self.rotary_embedding is not None:
embedding = self.rotary_embedding(q)
q, k, v = _apply_rotary_emb(q, k, v, embedding[None, ...])
energy = einsum("b h i d, b h j d -> b h i j", q, k) * self.scale
mask_value = -torch.finfo(energy.dtype).max
energy = apply_input_mask(
b, n, k, energy, input_mask, context, context_mask, mask_value, device
)
if self.causal:
energy = apply_causal_mask(energy, input_mask, mask_value, device)
attn = F.softmax(energy, dim=-1)
attn = self.dropout(attn)
out = einsum("b h i j, b h j d -> b h i d", attn, v)
out = rearrange(out, "b h n d -> b n (h d)")
out = self.fc(out)
return out
def _apply_rotary_emb(
q: Tensor, k: Tensor, v: Tensor, freqs: Tensor
) -> Tuple[Tensor, Tensor, Tensor]:
q, k, v = map(
lambda t: (t * freqs.cos()) + (rotate_half(t) * freqs.sin()), (q, k, v)
)
return q, k, v
def apply_input_mask(
b: int,
n: int,
k: Tensor,
energy: Tensor,
input_mask: Optional[Tensor],
context: Optional[Tensor],
context_mask: Optional[Tensor],
mask_value: Tensor,
device: str,
) -> Tensor:
"""Applies an input mask."""
if any(x is not None for x in (input_mask, context_mask)):
q_mask = (
input_mask
if input_mask is not None
else torch.ones((b, n), device=device).bool()
)
k_mask = q_mask if context is None else context_mask
k_mask = (
torch.ones((b, k.shape[-2]), device=device).bool()
if k_mask is None
else k_mask
)
q_mask = rearrange(q_mask, "b i -> b () i ()")
k_mask = rearrange(k_mask, "b j -> b () () j")
input_mask = q_mask * k_mask
energy = energy.masked_fill_(~input_mask, mask_value)
return energy
def apply_causal_mask(
energy: Tensor, mask: Tensor, mask_value: Tensor, device: str
) -> Tensor:
"""Applies a causal mask to the energy tensor."""
i, j = energy.shape[-2:]
r = torch.arange(i, device=device)
mask = rearrange(r, "i -> () () i ()") < rearrange(r, "j -> () () () j")
mask = F.pad(mask, (j - i, 0), value=False)
energy.masked_fill_(mask, mask_value)
return energy
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