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# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from functools import partial
import torch
import torch.nn as nn
from src.masks.utils import apply_masks
from src.models.utils.modules import Block
from src.models.utils.patch_embed import PatchEmbed, PatchEmbed3D
from src.models.utils.pos_embs import get_2d_sincos_pos_embed, get_3d_sincos_pos_embed
from src.utils.tensors import trunc_normal_
class VisionTransformer(nn.Module):
"""Vision Transformer"""
def __init__(
self,
img_size=(224, 224),
patch_size=16,
num_frames=1,
tubelet_size=2,
in_chans=3,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.0,
norm_layer=nn.LayerNorm,
init_std=0.02,
out_layers=None,
uniform_power=False,
use_silu=False,
wide_silu=True,
use_sdpa=True,
use_activation_checkpointing=False,
use_rope=False,
handle_nonsquare_inputs=True,
**kwargs
):
super().__init__()
self.num_features = self.embed_dim = embed_dim
self.num_heads = num_heads
self.out_layers = out_layers
self.handle_nonsquare_inputs = handle_nonsquare_inputs
if type(img_size) is int:
img_size = (img_size, img_size)
self.img_height, self.img_width = img_size
self.patch_size = patch_size
self.num_frames = num_frames
self.tubelet_size = tubelet_size
self.is_video = num_frames > 1
self.use_activation_checkpointing = use_activation_checkpointing
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
# Tokenize pixels with convolution
if self.is_video:
self.patch_embed = PatchEmbed3D(
patch_size=patch_size, tubelet_size=tubelet_size, in_chans=in_chans, embed_dim=embed_dim
)
self.num_patches = (num_frames // tubelet_size) * (img_size[0] // patch_size) * (img_size[1] // patch_size)
else:
self.patch_embed = PatchEmbed(patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
self.num_patches = (img_size[0] // patch_size) * (img_size[1] // patch_size)
# Position embedding
self.uniform_power = uniform_power
self.use_rope = use_rope
if self.use_rope:
self.pos_embed = None
else:
self.pos_embed = nn.Parameter(torch.zeros(1, self.num_patches, embed_dim), requires_grad=False)
# Attention Blocks
self.blocks = nn.ModuleList(
[
Block(
use_rope=use_rope,
grid_size=img_size[0] // patch_size,
grid_depth=num_frames // tubelet_size,
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
use_sdpa=use_sdpa,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop_rate,
act_layer=nn.SiLU if use_silu else nn.GELU,
wide_silu=wide_silu,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
)
for i in range(depth)
]
)
self.norm = norm_layer(embed_dim)
# ------ initialize weights
if self.pos_embed is not None:
self._init_pos_embed(self.pos_embed.data) # sincos pos-embed
self.init_std = init_std
self.apply(self._init_weights)
self._rescale_blocks()
def _init_pos_embed(self, pos_embed):
embed_dim = pos_embed.size(-1)
grid_size = self.img_height // self.patch_size # TODO: update; currently assumes square input
if self.is_video:
grid_depth = self.num_frames // self.tubelet_size
sincos = get_3d_sincos_pos_embed(
embed_dim, grid_size, grid_depth, cls_token=False, uniform_power=self.uniform_power
)
else:
sincos = get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False)
pos_embed.copy_(torch.from_numpy(sincos).float().unsqueeze(0))
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=self.init_std)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
trunc_normal_(m.weight, std=self.init_std)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Conv3d):
trunc_normal_(m.weight, std=self.init_std)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def _rescale_blocks(self):
def rescale(param, layer_id):
param.div_(math.sqrt(2.0 * layer_id))
for layer_id, layer in enumerate(self.blocks):
rescale(layer.attn.proj.weight.data, layer_id + 1)
rescale(layer.mlp.fc2.weight.data, layer_id + 1)
def get_num_layers(self):
return len(self.blocks)
def no_weight_decay(self):
return {}
def forward(self, x, masks=None):
"""
:param x: input image/video
:param masks: indices of patch tokens to mask (remove)
"""
if masks is not None and not isinstance(masks, list):
masks = [masks]
# Tokenize input
# Image
if x.ndim == 4:
_, _, H, W = x.shape
T = 1
# Video
elif x.ndim == 5:
_, _, T, H, W = x.shape
T = T // self.tubelet_size
H_patches = H // self.patch_size
W_patches = W // self.patch_size
if not self.handle_nonsquare_inputs:
T = H_patches = W_patches = None
if not self.use_rope:
pos_embed = self.interpolate_pos_encoding(x, self.pos_embed)
x = self.patch_embed(x)
x += pos_embed
else:
x = self.patch_embed(x)
# Mask away unwanted tokens (if masks provided)
if masks is not None:
x = apply_masks(x, masks)
masks = torch.cat(masks, dim=0)
# Fwd prop
outs = []
for i, blk in enumerate(self.blocks):
if self.use_activation_checkpointing:
x = torch.utils.checkpoint.checkpoint(
blk, x, masks, None, T=T, H_patches=H_patches, W_patches=W_patches, use_reentrant=False
)
else:
x = blk(x, mask=masks, attn_mask=None, T=T, H_patches=H_patches, W_patches=W_patches)
if self.out_layers is not None and i in self.out_layers:
outs.append(self.norm(x))
if self.out_layers is not None:
return outs
if self.norm is not None:
x = self.norm(x)
return x
def interpolate_pos_encoding(self, x, pos_embed):
_, N, dim = pos_embed.shape
if self.is_video:
# If pos_embed already corret size, just return
_, _, T, H, W = x.shape
if H == self.img_height and W == self.img_width and T == self.num_frames:
return pos_embed
# Just chop off last N tokens of positional embedding
elif H == self.img_height and W == self.img_width and T < self.num_frames:
new_N = int((T // self.tubelet_size) * (H // self.patch_size) * (W // self.patch_size))
return pos_embed[:, :new_N, :]
# Convert depth, height, width of input to be measured in patches
# instead of pixels/frames
T = T // self.tubelet_size
H = H // self.patch_size
W = W // self.patch_size
# Compute the initialized shape of the positional embedding measured
# in patches
N_t = self.num_frames // self.tubelet_size
N_h = self.img_height // self.patch_size
N_w = self.img_width // self.patch_size
assert N_h * N_w * N_t == N, "Positional embedding initialized incorrectly"
# Compute scale factor for spatio-temporal interpolation
scale_factor = (T / N_t, H / N_h, W / N_w)
pos_embed = nn.functional.interpolate(
pos_embed.reshape(1, N_t, N_h, N_w, dim).permute(0, 4, 1, 2, 3),
scale_factor=scale_factor,
mode="trilinear",
)
pos_embed = pos_embed.permute(0, 2, 3, 4, 1).view(1, -1, dim)
return pos_embed
else:
# If pos_embed already corret size, just return
_, _, H, W = x.shape
if H == self.img_height and W == self.img_width:
return pos_embed
# Compute scale factor for spatial interpolation
npatch = (H // self.patch_size) * (W // self.patch_size)
scale_factor = math.sqrt(npatch / N)
pos_embed = nn.functional.interpolate(
pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),
scale_factor=scale_factor,
mode="bicubic",
)
pos_embed = pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return pos_embed
def vit_large(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size,
embed_dim=1024,
depth=24,
num_heads=16,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
def vit_huge(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size,
embed_dim=1280,
depth=32,
num_heads=16,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
def vit_giant_xformers(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size,
embed_dim=1408,
depth=40,
num_heads=22,
mlp_ratio=48 / 11,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
# We do not use any of the following ViT definitions in V-JEPA 2, but retain them for
# compatibility reasons.
def vit_synthetic(patch_size=16, **kwargs):
# For performance testing only
model = VisionTransformer(
patch_size=patch_size,
embed_dim=1,
depth=1,
num_heads=1,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
def vit_tiny(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size,
embed_dim=192,
depth=12,
num_heads=3,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
def vit_small(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size,
embed_dim=384,
depth=12,
num_heads=6,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
def vit_base(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
def vit_large_rope(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size,
embed_dim=1024,
depth=24,
num_heads=16,
mlp_ratio=4,
qkv_bias=True,
use_rope=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
def vit_huge_rope(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size,
embed_dim=1280,
depth=32,
num_heads=16,
mlp_ratio=4,
qkv_bias=True,
use_rope=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
def vit_giant(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size,
embed_dim=1408,
depth=40,
num_heads=16,
mlp_ratio=48 / 11,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
def vit_giant_rope(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size,
embed_dim=1408,
depth=40,
num_heads=16,
mlp_ratio=48 / 11,
qkv_bias=True,
use_rope=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
def vit_giant_xformers_rope(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size,
embed_dim=1408,
depth=40,
num_heads=22,
mlp_ratio=48 / 11,
qkv_bias=True,
use_rope=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
def vit_gigantic(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size,
embed_dim=1664,
depth=48,
num_heads=16,
mpl_ratio=64 / 13,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
def vit_gigantic_xformers(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size,
embed_dim=1664,
depth=48,
num_heads=26,
mpl_ratio=64 / 13,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
VIT_EMBED_DIMS = {
"vit_synthetic": 1,
"vit_tiny": 192,
"vit_small": 384,
"vit_base": 768,
"vit_large": 1024,
"vit_huge": 1280,
"vit_giant": 1408,
"vit_gigantic": 1664,
}
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