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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
try:
from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint
except:
from torch.utils.checkpoint import checkpoint
def exists(val):
return val is not None
def FeedForward(
dim,
mult=4,
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 enable_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
class PerceiverAttention(nn.Module):
def __init__(
self,
*,
dim,
dim_head=64,
heads=8,
enable_init_network_params=False,
initializer_range=0.02,
):
super().__init__()
self.scale = dim_head**-0.5
self.heads = heads
self.initializer_range = initializer_range
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)
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, latents):
"""
Args:
x (torch.Tensor): image features
shape (b, T, n1, D)
latent (torch.Tensor): latent features
shape (b, T, n2, D)
"""
x = self.norm_media(x)
latents = self.norm_latents(latents.contiguous())
h = self.heads
q = self.to_q(latents)
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=6,
dim_head=64,
heads=8,
num_latents=64,
max_num_media=None,
max_num_frames=None,
ff_mult=4,
enable_init_network_params=False,
initializer_range=0.02,
gradient_checkpointing=False,
):
super().__init__()
self.gradient_checkpointing = gradient_checkpointing
self.initializer_range = initializer_range
self.latents = nn.Parameter(torch.randn(num_latents, dim))
self.frame_embs = (
nn.Parameter(torch.randn(max_num_frames, dim))
if exists(max_num_frames)
else None
)
self.media_time_embs = (
nn.Parameter(torch.randn(max_num_media, 1, dim))
if exists(max_num_media)
else None
)
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(
nn.ModuleList(
[
PerceiverAttention(
dim=dim,
dim_head=dim_head,
heads=heads,
enable_init_network_params=enable_init_network_params,
initializer_range=initializer_range,
),
FeedForward(
dim=dim,
mult=ff_mult,
enable_init_network_params=enable_init_network_params,
initializer_range=initializer_range,
),
]
)
)
# Should this norm layer also change?
self.norm = nn.LayerNorm(dim)
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)
elif isinstance(module, nn.Parameter):
module.data.normal_(mean=0.0, std=self.initializer_range)
def forward(self, x):
"""
Args:
x (torch.Tensor): image features
shape (b, T, F, v, D)
Returns:
shape (b, T, n, D) where n is self.num_latents
"""
b, T, F, v = x.shape[:4]
# frame and media time embeddings
if exists(self.frame_embs):
frame_embs = repeat(self.frame_embs[:F], "F d -> b T F v d", b=b, T=T, v=v)
x = x + frame_embs
x = rearrange(
x, "b T F v d -> b T (F v) d"
) # flatten the frame and spatial dimensions
if exists(self.media_time_embs):
x = x + self.media_time_embs[:T]
# blocks
latents = repeat(self.latents, "n d -> b T n d", b=b, T=T)
for attn, ff in self.layers:
if self.gradient_checkpointing and latents.requires_grad:
latents = checkpoint(attn, x, (latents)) + latents
latents = checkpoint(ff, latents) + latents
else:
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_visual,
dim_head=64,
heads=8,
only_attend_immediate_media=True,
enable_init_network_params=False,
initializer_range=0.02,
):
super().__init__()
self.scale = dim_head**-0.5
self.heads = heads
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):
"""
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(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=8,
ff_mult=4,
only_attend_immediate_media=True,
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,
enable_init_network_params=enable_init_network_params,
initializer_range=initializer_range,
)
self.attn_gate = nn.Parameter(torch.tensor([0.0]))
self.ff = FeedForward(dim, mult=ff_mult)
self.ff_gate = nn.Parameter(torch.tensor([0.0]))
self.gradient_checkpointing = gradient_checkpointing
def forward(
self,
x,
media,
media_locations=None,
use_cached_media=False,
):
if exists(media_locations):
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)
else:
flag = 1.0
if self.gradient_checkpointing and media.requires_grad:
x = (
flag
* checkpoint(self.attn, x, media, media_locations, use_cached_media)
* self.attn_gate.tanh()
+ x
)
x = flag * checkpoint(self.ff, x) * self.ff_gate.tanh() + x
else:
x = (
flag
* self.attn(
x,
media,
media_locations=media_locations,
use_cached_media=use_cached_media,
)
* self.attn_gate.tanh()
+ x
)
x = flag * self.ff(x) * self.ff_gate.tanh() + x
return x
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