File size: 10,637 Bytes
c6be546 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 | import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from beartype import beartype
from typing import Tuple
from einops import rearrange, repeat
# helpers
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def leaky_relu(p = 0.1):
return nn.LeakyReLU(p)
def l2norm(t):
return F.normalize(t, dim = -1)
# bias-less layernorm, being used in more recent T5s, PaLM, also in @borisdayma 's experiments shared with me
# greater stability
class LayerNorm(nn.Module):
def __init__(self, dim):
super().__init__()
self.gamma = nn.Parameter(torch.ones(dim))
self.register_buffer("beta", torch.zeros(dim))
def forward(self, x):
return F.layer_norm(x, x.shape[-1:],
self.gamma.to(x.dtype), self.beta.to(x.dtype))
# feedforward
class GEGLU(nn.Module):
def forward(self, x):
x, gate = x.chunk(2, dim = -1)
return F.gelu(gate) * x
def FeedForward(dim, mult = 4, dropout = 0.):
inner_dim = int(mult * (2 / 3) * dim)
return nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, inner_dim * 2, bias = False),
GEGLU(),
nn.Dropout(dropout),
nn.Linear(inner_dim, dim, bias = False)
)
# PEG - position generating module
class PEG(nn.Module):
def __init__(self, dim, causal = False):
super().__init__()
self.causal = causal
self.dsconv = nn.Conv3d(dim, dim, 3, groups = dim)
@beartype
def forward(self, x, shape: Tuple[int, int, int, int] = None):
needs_shape = x.ndim == 3
assert not (needs_shape and not exists(shape))
orig_shape = x.shape
if needs_shape:
x = x.reshape(*shape, -1)
x = rearrange(x, 'b ... d -> b d ...')
frame_padding = (2, 0) if self.causal else (1, 1)
x = F.pad(x, (1, 1, 1, 1, *frame_padding), value = 0.)
x = self.dsconv(x)
x = rearrange(x, 'b d ... -> b ... d')
if needs_shape:
x = rearrange(x, 'b ... d -> b (...) d')
return x.reshape(orig_shape)
# attention
class Attention(nn.Module):
def __init__(
self,
dim,
dim_context = None,
dim_head = 64,
heads = 8,
causal = False,
num_null_kv = 0,
norm_context = True,
dropout = 0.,
scale = 8
):
super().__init__()
self.heads = heads
self.causal = causal
self.scale = scale
inner_dim = dim_head * heads
dim_context = default(dim_context, dim)
if causal:
self.rel_pos_bias = AlibiPositionalBias(heads = heads)
self.attn_dropout = nn.Dropout(dropout)
self.norm = LayerNorm(dim)
self.context_norm = LayerNorm(dim_context) if norm_context else nn.Identity()
self.num_null_kv = num_null_kv
# Only create null_kv parameter if num_null_kv > 0
# Zero-element parameters cause DeepSpeed ZeRO partitioning issues
if num_null_kv > 0:
self.null_kv = nn.Parameter(torch.randn(heads, 2 * num_null_kv, dim_head))
else:
self.register_buffer('null_kv', None)
self.to_q = nn.Linear(dim, inner_dim, bias = False)
self.to_kv = nn.Linear(dim_context, inner_dim * 2, bias = False)
self.q_scale = nn.Parameter(torch.ones(dim_head))
self.k_scale = nn.Parameter(torch.ones(dim_head))
self.to_out = nn.Linear(inner_dim, dim, bias = False)
def forward(
self,
x,
mask = None,
context = None,
attn_bias = None
):
batch, device, dtype = x.shape[0], x.device, x.dtype
device=torch.device('cuda')
if exists(context):
context = self.context_norm(context)
kv_input = default(context, x)
x = self.norm(x)
q, k, v = self.to_q(x), *self.to_kv(kv_input).chunk(2, dim = -1)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), (q, k, v))
# Only add null key-values if they exist
if self.null_kv is not None:
nk, nv = repeat(self.null_kv, 'h (n r) d -> b h n r d', b = batch, r = 2).unbind(dim = -2)
k = torch.cat((nk, k), dim = -2)
v = torch.cat((nv, v), dim = -2)
q, k = map(l2norm, (q, k))
q = q * self.q_scale
k = k * self.k_scale
sim = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
i, j = sim.shape[-2:]
if exists(attn_bias):
attn_bias = F.pad(attn_bias, (self.num_null_kv, 0), value = 0.)
sim = sim + attn_bias
if exists(mask):
mask = F.pad(mask, (self.num_null_kv, 0), value = True)
mask = rearrange(mask, 'b j -> b 1 1 j')
sim = sim.masked_fill(~mask, -torch.finfo(sim.dtype).max)
if self.causal:
sim = sim + self.rel_pos_bias(sim)
device=torch.device('cuda')
causal_mask = torch.ones((i, j), device = device, dtype = torch.bool).triu(j - i + 1)
sim = sim.masked_fill(causal_mask, -torch.finfo(sim.dtype).max)
attn = sim.softmax(dim = -1)
attn = self.attn_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)')
return self.to_out(out)
# alibi positional bias for extrapolation
class AlibiPositionalBias(nn.Module):
def __init__(self, heads):
super().__init__()
self.heads = heads
slopes = torch.Tensor(self._get_slopes(heads))
slopes = rearrange(slopes, 'h -> h 1 1')
self.register_buffer('slopes', slopes, persistent = False)
self.register_buffer('bias', None, persistent = False)
def get_bias(self, i, j, device):
device=torch.device('cuda')
i_arange = torch.arange(j - i, j, device = device)
j_arange = torch.arange(j, device = device)
bias = -torch.abs(rearrange(j_arange, 'j -> 1 1 j') - rearrange(i_arange, 'i -> 1 i 1'))
return bias
@staticmethod
def _get_slopes(heads):
def get_slopes_power_of_2(n):
start = (2**(-2**-(math.log2(n)-3)))
ratio = start
return [start*ratio**i for i in range(n)]
if math.log2(heads).is_integer():
return get_slopes_power_of_2(heads)
closest_power_of_2 = 2 ** math.floor(math.log2(heads))
return get_slopes_power_of_2(closest_power_of_2) + get_slopes_power_of_2(2 * closest_power_of_2)[0::2][:heads-closest_power_of_2]
def forward(self, sim):
h, i, j, device = *sim.shape[-3:], sim.device
if exists(self.bias) and self.bias.shape[-1] >= j:
return self.bias[..., :i, :j]
device=torch.device('cuda')
bias = self.get_bias(i, j, device)
bias = bias * self.slopes
num_heads_unalibied = h - bias.shape[0]
bias = F.pad(bias, (0, 0, 0, 0, 0, num_heads_unalibied))
self.register_buffer('bias', bias, persistent = False)
return self.bias
class ContinuousPositionBias(nn.Module):
""" from https://arxiv.org/abs/2111.09883 """
def __init__(
self,
*,
dim,
heads,
num_dims = 2, # 2 for images, 3 for video
layers = 2,
log_dist = True,
cache_rel_pos = False
):
super().__init__()
self.num_dims = num_dims
self.log_dist = log_dist
self.net = nn.ModuleList([])
self.net.append(nn.Sequential(nn.Linear(self.num_dims, dim), leaky_relu()))
for _ in range(layers - 1):
self.net.append(nn.Sequential(nn.Linear(dim, dim), leaky_relu()))
self.net.append(nn.Linear(dim, heads))
self.cache_rel_pos = cache_rel_pos
self.register_buffer('rel_pos', None, persistent = False)
def forward(self, *dimensions, device = torch.device('cpu')):
if not exists(self.rel_pos) or not self.cache_rel_pos:
device=torch.device('cuda')
positions = [torch.arange(d, device = device) for d in dimensions]
grid = torch.stack(torch.meshgrid(*positions, indexing = 'ij'))
grid = rearrange(grid, 'c ... -> (...) c')
rel_pos = rearrange(grid, 'i c -> i 1 c') - rearrange(grid, 'j c -> 1 j c')
if self.log_dist:
rel_pos = torch.sign(rel_pos) * torch.log(rel_pos.abs() + 1)
self.register_buffer('rel_pos', rel_pos, persistent = False)
# Get the dtype of the model weights for consistent computation
target_dtype = self.net[0][0].weight.dtype if len(self.net) > 0 else torch.float32
rel_pos = self.rel_pos.to(target_dtype)
for layer in self.net:
rel_pos = layer(rel_pos)
return rearrange(rel_pos, 'i j h -> h i j')
# transformer
class Transformer(nn.Module):
def __init__(
self,
dim,
*,
depth,
dim_context = None,
causal = False,
dim_head = 64,
heads = 8,
ff_mult = 4,
peg = False,
peg_causal = False,
attn_num_null_kv = 2,
has_cross_attn = False,
attn_dropout = 0.,
ff_dropout = 0.
):
super().__init__()
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([
PEG(dim = dim, causal = peg_causal) if peg else None,
Attention(dim = dim, dim_head = dim_head, heads = heads, causal = causal, dropout = attn_dropout),
Attention(dim = dim, dim_head = dim_head, dim_context = dim_context, heads = heads, causal = False, num_null_kv = attn_num_null_kv, dropout = attn_dropout) if has_cross_attn else None,
FeedForward(dim = dim, mult = ff_mult, dropout = ff_dropout)
]))
self.norm_out = LayerNorm(dim)
@beartype
def forward(
self,
x,
video_shape: Tuple[int, int, int, int] = None,
attn_bias = None,
context = None,
self_attn_mask = None,
cross_attn_context_mask = None
):
for peg, self_attn, cross_attn, ff in self.layers:
if exists(peg):
x = peg(x, shape = video_shape) + x
x = self_attn(x, attn_bias = attn_bias, mask = self_attn_mask) + x
if exists(cross_attn) and exists(context):
x = cross_attn(x, context = context, mask = cross_attn_context_mask) + x
x = ff(x) + x
return self.norm_out(x)
|