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import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, repeat
from einops_exts import rearrange_many, repeat_many
import pdb
def exists(val):
return val is not None
def FeedForward(dim, mult = 4):
inner_dim = int(dim * mult)
return nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, inner_dim, bias = False),
nn.GELU(),
nn.Linear(inner_dim, dim, bias = False)
)
class PerceiverAttention(nn.Module):
def __init__(
self,
*,
dim,
dim_head = 64,
heads = 8
):
super().__init__()
self.scale = dim_head ** -0.5
self.heads = heads
inner_dim = dim_head * heads
self.norm_media = nn.LayerNorm(dim)
self.norm_latents = nn.LayerNorm(dim)
self.to_q = nn.Linear(dim, inner_dim, bias = False)
self.to_kv = nn.Linear(dim, inner_dim * 2, bias = False)
self.to_out = nn.Linear(inner_dim, dim, bias = False)
def forward(self, x, latents):
"""
einstein notation
b - batch
t - time
n - sequence
d - dimension
"""
x = self.norm_media(x)
latents = self.norm_latents(latents)
b, m, h = *x.shape[:2], self.heads
q = self.to_q(latents)
# the paper differs from Perceiver in which they also concat the key / values derived from the latents to be attended to
kv_input = torch.cat((x, latents), dim = -2)
k, v = self.to_kv(kv_input).chunk(2, dim = -1)
q, k, v = rearrange_many((q, k, v), 'b t n (h d) -> b h t n d', h = h)
q = q * self.scale
# attention
sim = einsum('... i d, ... j d -> ... i j', q, k)
sim = sim - sim.amax(dim = -1, keepdim = True).detach()
attn = sim.softmax(dim = -1)
out = einsum('... i j, ... j d -> ... i d', attn, v)
out = rearrange(out, 'b h t n d -> b t n (h d)', h = h)
return self.to_out(out)
class PerceiverResampler(nn.Module):
def __init__(
self,
*,
dim,
depth,
dim_head = 64,
heads = 8,
num_latents = 64,
num_time_embeds = 4,
ff_mult = 4,
inp_dim=None,
):
super().__init__()
self.latents = nn.Parameter(torch.randn(num_latents, dim))
self.time_pos_emb = nn.Parameter(torch.randn(num_time_embeds, 1, dim))
if inp_dim is not None:
self.inp_linear = nn.Linear(inp_dim, dim, bias=False)
else:
self.inp_linear = None
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([
PerceiverAttention(dim = dim, dim_head = dim_head, heads = heads),
FeedForward(dim = dim, mult = ff_mult)
]))
self.norm = nn.LayerNorm(dim)
def forward(self, x):
if x.ndim == 3:
x = rearrange(x, 'b n d -> b 1 n d')
if self.inp_linear is not None:
x = self.inp_linear(x)
times = x.shape[1]
x = x + self.time_pos_emb[:times]
latents = repeat(self.latents, 'n d -> b m n d', b = x.shape[0], m = x.shape[1])
for attn, ff in self.layers:
latents = attn(x, latents) + latents
latents = ff(latents) + latents
return self.norm(latents)
# gated cross attention
class MaskedCrossAttention(nn.Module):
def __init__(
self,
*,
dim,
dim_head = 64,
heads = 8,
only_attend_immediate_media = True
):
super().__init__()
self.scale = dim_head ** -0.5
self.heads = heads
inner_dim = dim_head * heads
self.norm = nn.LayerNorm(dim)
self.to_q = nn.Linear(dim, inner_dim, bias = False)
self.to_kv = nn.Linear(dim, inner_dim * 2, bias = False)
self.to_out = nn.Linear(inner_dim, dim, bias = False)
# whether for text to only attend to immediate preceding image, or all images
self.only_attend_immediate_media = only_attend_immediate_media
def forward(
self,
x,
media,
media_locations = None
):
b, t, m = media.shape[:3]
h = self.heads
x = self.norm(x)
q = self.to_q(x)
media = rearrange(media, 'b t n d -> b (t n) d')
k, v = self.to_kv(media).chunk(2, dim = -1)
q, k, v = rearrange_many((q, k, v), 'b n (h d) -> b h n d', h = h)
q = q * self.scale
sim = einsum('... i d, ... j d -> ... i j', q, k)
if exists(media_locations):
text_time = media_locations.cumsum(dim = -1) # at each boolean of True, increment the time counter (relative to media time)
media_time = torch.arange(t, device = x.device) + 1
# text time must equal media time if only attending to most immediate image
# otherwise, as long as text time is greater than media time (if attending to all previous images / media)
mask_op = torch.eq if self.only_attend_immediate_media else torch.ge
text_to_media_mask = mask_op(rearrange(text_time, 'b i -> b 1 i 1'), repeat(media_time, 'j -> 1 1 1 (j m)', m = m))
sim = sim.masked_fill(~text_to_media_mask, -torch.finfo(sim.dtype).max)
sim = sim - sim.amax(dim = -1, keepdim = True).detach()
attn = sim.softmax(dim = -1)
if exists(media_locations) and self.only_attend_immediate_media:
# any text without a preceding media needs to have attention zeroed out
text_without_media_mask = text_time == 0
text_without_media_mask = rearrange(text_without_media_mask, 'b i -> b 1 i 1')
attn.masked_fill(text_without_media_mask, 0.)
out = einsum('... i j, ... j d -> ... i d', attn, v)
out = rearrange(out, 'b h n d -> b n (h d)')
return self.to_out(out)
class GatedCrossAttentionBlock(nn.Module):
def __init__(
self,
*,
dim,
dim_head = 64,
heads = 8,
ff_mult = 4,
only_attend_immediate_media = True
):
super().__init__()
self.attn = MaskedCrossAttention(dim = dim, dim_head = dim_head, heads = heads, only_attend_immediate_media = only_attend_immediate_media)
self.attn_gate = nn.Parameter(torch.tensor([0.]))
self.ff = FeedForward(dim, mult = ff_mult)
self.ff_gate = nn.Parameter(torch.tensor([0.]))
def forward(
self,
x,
media, # media tensor, encoded by perceiver resample - (batch, time, latents, dim)
media_locations = None # boolean tensor indicating positions of media - (batch, sequence)
):
x = self.attn(x, media, media_locations = media_locations) * self.attn_gate.tanh() + x
x = self.ff(x) * self.ff_gate.tanh() + x
return x
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