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"""
Based on: https://github.com/lucidrains/flamingo-pytorch
"""
import torch
from einops import rearrange, repeat
from torch import einsum, nn
from einops_exts import rearrange_many
def exists(val):
return val is not None
def FeedForward(
dim,
mult=4,
use_ft_layernorm=False,
enable_init_network_params=False,
initializer_range=0.02,
):
inner_dim = int(dim * mult)
net = nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, inner_dim, bias=False),
nn.GELU(),
nn.Linear(inner_dim, dim, bias=False),
)
if use_ft_layernorm and enable_init_network_params:
# only use_ft_layernorm is on and enalbe_init_network_params
# then start the initialization
net[0].weight.data.normal_(mean=0.0, std=initializer_range)
net[0].bias.data.zero_()
net[1].weight.data.normal_(mean=0.0, std=initializer_range)
net[3].weight.data.normal_(mean=0.0, std=initializer_range)
return net
# gated cross attention
class MaskedCrossAttention(nn.Module):
def __init__(
self,
*,
dim,
dim_visual,
dim_head=64,
heads=8,
only_attend_immediate_media=True,
use_ft_layernorm=False,
use_ft_flash_attention=False,
enable_init_network_params=False,
initializer_range=0.02,
):
super().__init__()
self.scale = dim_head**-0.5
self.heads = heads
self.use_ft_flash_attention = False
self.initializer_range = initializer_range
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_visual, 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 previous images
self.only_attend_immediate_media = only_attend_immediate_media
if enable_init_network_params:
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def forward(self, x, media, media_locations=None, use_cached_media=False, image_mask=None):
"""
Args:
x (torch.Tensor): text features
shape (B, T_txt, D_txt)
media (torch.Tensor): image features
shape (B, T_img, n, D_img) where n is the dim of the latents
media_locations: boolean mask identifying the media tokens in x
shape (B, T_txt)
use_cached_media: bool
If true, treat all of x as if they occur after the last media
registered in media_locations. T_txt does not need to exactly
equal media_locations.shape[1] in this case
"""
if not use_cached_media:
assert media_locations.shape[1] == x.shape[1], (
f"media_location.shape is {media_locations.shape} but x.shape is"
f" {x.shape}"
)
T_txt = x.shape[1]
_, T_img, n = media.shape[:3]
h = self.heads
x = self.norm(x.contiguous())
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)
if exists(media_locations):
media_time = torch.arange(T_img, device=x.device) + 1
if use_cached_media:
# text time is set to the last cached media location
text_time = repeat(
torch.count_nonzero(media_locations, dim=1),
"b -> b i",
i=T_txt,
)
else:
# at each boolean of True, increment the time counter (relative to media time)
text_time = media_locations.cumsum(dim=-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 n)", n=n),
)
if 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"
)
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(image_mask):
image_mask = image_mask.unsqueeze(1).unsqueeze(1).bool()
image_mask = image_mask.repeat_interleave(int(sim.shape[3] / image_mask.shape[3]), dim=-1)
sim = sim.masked_fill(~image_mask, -torch.finfo(sim.dtype).max)
# if exists(media_locations):
# 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
attn = attn.masked_fill(text_without_media_mask, 0.0)
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_visual,
dim_head=64,
heads=12,
ff_mult=1,
only_attend_immediate_media=True,
use_ft_layernorm=False,
use_ft_flash_attention=False,
enable_init_network_params=False,
initializer_range=0.02,
gradient_checkpointing=False,
):
super().__init__()
self.attn = MaskedCrossAttention(
dim=dim,
dim_visual=dim_visual,
dim_head=dim_head,
heads=heads,
only_attend_immediate_media=only_attend_immediate_media,
use_ft_flash_attention=use_ft_flash_attention,
use_ft_layernorm=use_ft_layernorm,
enable_init_network_params=enable_init_network_params,
initializer_range=initializer_range,
)
self.attn_gate = nn.Parameter(torch.zeros(dim))
self.ff = FeedForward(dim, mult=ff_mult)
self.ff_gate = nn.Parameter(torch.zeros(dim))
self.gradient_checkpointing = gradient_checkpointing
def forward(
self,
x,
media,
media_locations=None,
use_cached_media=False,
image_mask=None,
):
flag = torch.sum(media_locations, dim=-1)
flag = torch.where(flag > 0.0, 1.0, 0.0)
flag = flag.unsqueeze(1).unsqueeze(1).to(torch.bfloat16)
x = (
flag
* self.attn(
x,
media,
media_locations=media_locations,
use_cached_media=use_cached_media,
image_mask=image_mask,
)
* self.attn_gate.tanh()
+ x
)
x = flag * self.ff(x) * self.ff_gate.tanh() + x
return x
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