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"""Implementes the attention module for the transformer."""
from typing import Optional, Tuple
import attr
from einops import rearrange
import torch
from torch import einsum
from torch import nn
from torch import Tensor
import torch.nn.functional as F
from text_recognizer.networks.transformer.embeddings.rotary import (
RotaryEmbedding,
rotate_half,
)
@attr.s(eq=False)
class Attention(nn.Module):
"""Standard attention."""
def __attrs_pre_init__(self) -> None:
"""Pre init constructor."""
super().__init__()
dim: int = attr.ib()
num_heads: int = attr.ib()
causal: bool = attr.ib(default=False)
dim_head: int = attr.ib(default=64)
dropout_rate: float = attr.ib(default=0.0)
rotary_embedding: Optional[RotaryEmbedding] = attr.ib(default=None)
scale: float = attr.ib(init=False)
dropout: nn.Dropout = attr.ib(init=False)
fc: nn.Linear = attr.ib(init=False)
def __attrs_post_init__(self) -> None:
"""Post init configuration."""
self.scale = self.dim ** -0.5
inner_dim = self.num_heads * self.dim_head
self.query = nn.Linear(self.dim, inner_dim, bias=False)
self.key = nn.Linear(self.dim, inner_dim, bias=False)
self.value = 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.query(x)
k = self.key(context) if context is not None else self.key(x)
v = self.value(context) if context is not None else self.value(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)
del input_mask
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)
del mask
return energy
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