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1d998f6 | 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 | import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from ...ttt.layer import TTTWrapper
try:
import xformers.ops as xops
except ImportError as e:
xops = None
# https://github.com/karpathy/nanoGPT/blob/eba36e84649f3c6d840a93092cb779a260544d08/model.py#L162-L168
def _init_weights(module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
class MLP(nn.Module):
"""
MLP layer
Reference: https://github.com/facebookresearch/dino/blob/7c446df5b9f45747937fb0d72314eb9f7b66930a/vision_transformer.py#L49-L65
Ignore: https://github.com/facebookresearch/dino/blob/7c446df5b9f45747937fb0d72314eb9f7b66930a/vision_transformer.py#L62
"""
def __init__(
self,
d,
mlp_ratio=4,
mlp_bias=False,
mlp_dropout=0.0,
mlp_dim=None,
):
super().__init__()
if mlp_dim is None:
mlp_dim = d * mlp_ratio
self.mlp = nn.Sequential(
nn.Linear(d, mlp_dim, bias=mlp_bias),
nn.GELU(),
nn.Linear(mlp_dim, d, bias=mlp_bias),
nn.Dropout(mlp_dropout),
)
def forward(self, x):
x = self.mlp(x)
return x
class SelfAttention(nn.Module):
"""
Self-attention layer
Reference: https://github.com/facebookresearch/dino/blob/7c446df5b9f45747937fb0d72314eb9f7b66930a/vision_transformer.py#L68-L92
"""
def __init__(
self,
d,
d_head,
attn_qkv_bias=False,
attn_dropout=0.0,
attn_fc_bias=False,
attn_fc_dropout=0.0,
use_flashatt=False,
qk_norm=False,
rope=None,
):
super().__init__()
assert (
d % d_head == 0
), f"Token dimension {d} should be divisible by head dimension {d_head}"
self.d = d
self.d_head = d_head
self.attn_dropout = attn_dropout
self.to_qkv = nn.Linear(d, 3 * d, bias=attn_qkv_bias)
self.fc = nn.Linear(d, d, bias=attn_fc_bias)
self.attn_fc_dropout = nn.Dropout(attn_fc_dropout)
self.use_flashatt = use_flashatt
# QK norm are strictly RMSNorm, following SD3, section 5.3.2
norm_func = nn.RMSNorm
self.q_norm = norm_func(self.d_head) if qk_norm else nn.Identity()
self.k_norm = norm_func(self.d_head) if qk_norm else nn.Identity()
self.rope = rope
def forward(self, x, pos=None):
"""
x: (b, l, d)
"""
# token split, multi-head attention, token cat
q, k, v = self.to_qkv(x).split(self.d, dim=2)
if self.use_flashatt:
if xops is None:
raise RuntimeError("xformers is required when use_flashatt=true")
# Use the flash attention support from the xformers library
# The memory_efficient_attention takes the input as (batch, seq_len, heads, dim)
q, k, v = map(
lambda t: rearrange(t, "b l (nh dh) -> b l nh dh", dh=self.d_head).contiguous() ,
(q, k, v),
)
q, k = self.q_norm(q).to(v.dtype), self.k_norm(k).to(v.dtype)
if self.rope is not None and not pos is None:
q, k = map(lambda t: rearrange(t, "b l nh dh -> b nh l dh").contiguous() , (q, k),)
q = self.rope(q, pos)
k = self.rope(k, pos)
q, k = map(lambda t: rearrange(t, "b nh l dh -> b l nh dh").contiguous() , (q, k),)
# The memory_efficient_attention takes the input as (batch, seq_len, heads, dim)
flash3 = getattr(xops.fmha, "flash3", None)
use_flash3 = flash3 is not None and self.d_head in [64, 128, 192, 256]
op = (flash3.FwOp, flash3.BwOp) if use_flash3 else (xops.fmha.flash.FwOp, xops.fmha.flash.BwOp)
x = xops.memory_efficient_attention(
q,
k,
v,
attn_bias=None,
op=op,
)
x = rearrange(x, "b l nh dh -> b l (nh dh)")
else:
# Use the flash attention support from the pytorch library
q, k, v = (
rearrange(q, "b l (nh dh) -> b nh l dh", dh=self.d_head),
rearrange(k, "b l (nh dh) -> b nh l dh", dh=self.d_head),
rearrange(v, "b l (nh dh) -> b nh l dh", dh=self.d_head),
)
q, k = self.q_norm(q), self.k_norm(k)
if self.rope is not None:
q = self.rope(q, pos)
k = self.rope(k, pos)
# https://discuss.pytorch.org/t/flash-attention/174955/14
dropout_p = self.attn_dropout if self.training else 0.0
x = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)
x = rearrange(x, "b nh l dh -> b l (nh dh)")
x = self.attn_fc_dropout(self.fc(x))
return x
class TransformerBlock(nn.Module):
"""
Transformer block
Reference: https://github.com/facebookresearch/dino/blob/7c446df5b9f45747937fb0d72314eb9f7b66930a/vision_transformer.py#L95-L113
Note: move drop_path to SelfAttention and MLP
"""
def __init__(
self,
d,
d_head,
ln_bias=False,
attn_qkv_bias=False,
attn_dropout=0.0,
attn_fc_bias=False,
attn_fc_dropout=0.0,
mlp_ratio=4,
mlp_bias=False,
mlp_dropout=0.0,
use_flashatt=False,
qk_norm=False,
ttt_layer_cfg=None,
fix_attention=False,
rope=None,
):
super().__init__()
self.fix_attention = fix_attention
self.rope = rope
if ttt_layer_cfg is None:
raise ValueError("Release transformer expects a TTT layer configuration")
self.norm1 = nn.LayerNorm(d, bias=ln_bias)
self.attn = SelfAttention(
d, d_head, attn_qkv_bias, attn_dropout, attn_fc_bias, attn_fc_dropout,
use_flashatt=use_flashatt, qk_norm=qk_norm,
rope=self.rope,
)
self.norm2 = nn.LayerNorm(d, bias=ln_bias)
self.mlp = MLP(d, mlp_ratio, mlp_bias, mlp_dropout)
if self.fix_attention:
for param in self.attn.parameters():
param.requires_grad = False
self.target_norm1 = self.norm1
self.target_attn = self.attn
self.target_norm2 = self.norm2
self.target_mlp = self.mlp
self.ttt_layer = SeqModelingBlock(config=ttt_layer_cfg, rope=self.rope)
@staticmethod
def run_local_attention(x, atten_func, norm_func, tokens_per_frame, pos):
x_reshaped = x.reshape(-1, tokens_per_frame, x.shape[2])
if pos is not None:
pos = pos.reshape(-1, tokens_per_frame, pos.shape[2])
return atten_func(norm_func(x_reshaped), pos).reshape_as(x)
def forward(self, context_tokens, target_tokens, context_pos, target_pos, tokens_per_frame):
context_residual = self.run_local_attention(
context_tokens, self.attn, self.norm1, tokens_per_frame, context_pos
)
target_residual = self.run_local_attention(
target_tokens, self.target_attn, self.target_norm1,
tokens_per_frame, target_pos,
)
context_tokens = context_tokens + context_residual
target_tokens = target_tokens + target_residual
context_residual, target_residual = self.ttt_layer(
context_tokens, target_tokens, context_pos, target_pos
)
context_tokens = context_tokens + context_residual
target_tokens = target_tokens + target_residual
context_tokens = context_tokens + self.mlp(self.norm2(context_tokens))
target_tokens = target_tokens + self.mlp(self.norm2(target_tokens))
return context_tokens, target_tokens
def prefill_context(self, context_tokens, context_pos, tokens_per_frame):
context_residual = self.run_local_attention(
context_tokens,
self.attn,
self.norm1,
tokens_per_frame,
context_pos,
)
context_tokens = context_tokens + context_residual
context_residual, fast_weights = self.ttt_layer.prefill(
context_tokens, context_tokens, context_pos
)
context_tokens = context_tokens + context_residual
context_tokens = context_tokens + self.mlp(self.norm2(context_tokens))
return context_tokens, fast_weights
def render_target(self, target_tokens, target_pos, tokens_per_frame, fast_weights):
target_residual = self.run_local_attention(
target_tokens,
self.target_attn,
self.target_norm1,
tokens_per_frame,
target_pos,
)
target_tokens = target_tokens + target_residual
target_residual = self.ttt_layer.render(
target_tokens, target_pos, fast_weights
)
target_tokens = target_tokens + target_residual
return target_tokens + self.mlp(self.norm2(target_tokens))
class SeqModelingBlock(nn.Module):
def __init__(self, config, rope=None):
super().__init__()
self.ssm = TTTWrapper(config, rope=rope)
def forward(self, context, target, context_pos, target_pos):
queries = torch.cat([context, target], dim=1)
positions = torch.cat([context_pos, target_pos], dim=1)
output = self.ssm(context, context, queries, context_pos, positions)
return output[:, :context.shape[1]], output[:, context.shape[1]:]
def prefill(self, values, keys, key_pos):
return self.ssm.prefill(values, keys, key_pos)
def render(self, queries, query_pos, fast_weights):
return self.ssm.render(queries, query_pos, fast_weights)
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