File size: 10,382 Bytes
d9bb75c | 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 | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This software may be used and distributed in accordance with
# the terms of the DINOv3 License Agreement.
from typing import Callable, List, Optional
import torch
from torch import Tensor, nn
from dinov3.utils import cat_keep_shapes, uncat_with_shapes
from .attention import CausalSelfAttention, SelfAttention
from .ffn_layers import Mlp
from .layer_scale import LayerScale # , DropPath
torch._dynamo.config.automatic_dynamic_shapes = False
torch._dynamo.config.accumulated_cache_size_limit = 1024
class SelfAttentionBlock(nn.Module):
def __init__(
self,
dim: int,
num_heads: int,
ffn_ratio: float = 4.0,
qkv_bias: bool = False,
proj_bias: bool = True,
ffn_bias: bool = True,
drop: float = 0.0,
attn_drop: float = 0.0,
init_values=None,
drop_path: float = 0.0,
act_layer: Callable[..., nn.Module] = nn.GELU,
norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
attn_class: Callable[..., nn.Module] = SelfAttention,
ffn_layer: Callable[..., nn.Module] = Mlp,
mask_k_bias: bool = False,
device=None,
) -> None:
super().__init__()
# print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}")
self.norm1 = norm_layer(dim)
self.attn = attn_class(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
proj_bias=proj_bias,
attn_drop=attn_drop,
proj_drop=drop,
mask_k_bias=mask_k_bias,
device=device,
)
self.ls1 = LayerScale(dim, init_values=init_values, device=device) if init_values else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * ffn_ratio)
self.mlp = ffn_layer(
in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=drop,
bias=ffn_bias,
device=device,
)
self.ls2 = LayerScale(dim, init_values=init_values, device=device) if init_values else nn.Identity()
self.sample_drop_ratio = drop_path
@staticmethod
def _maybe_index_rope(rope: tuple[Tensor, Tensor] | None, indices: Tensor) -> tuple[Tensor, Tensor] | None:
if rope is None:
return None
sin, cos = rope
assert sin.ndim == cos.ndim
if sin.ndim == 4:
# If the rope embedding has a batch dimension (is different for each batch element), index into it
return sin[indices], cos[indices] # [batch, heads, patches, embed_dim]
else:
# No batch dimension, do not index
return sin, cos # [heads, patches, embed_dim] or [patches, embed_dim]
def _forward(self, x: Tensor, rope=None) -> Tensor:
"""
This is the reference implementation for a single tensor, matching what is done below for a list.
We call the list op on [x] instead of this function.
"""
b, _, _ = x.shape
sample_subset_size = max(int(b * (1 - self.sample_drop_ratio)), 1)
residual_scale_factor = b / sample_subset_size
if self.training and self.sample_drop_ratio > 0.0:
indices_1 = (torch.randperm(b, device=x.device))[:sample_subset_size]
x_subset_1 = x[indices_1]
rope_subset = self._maybe_index_rope(rope, indices_1)
residual_1 = self.attn(self.norm1(x_subset_1), rope=rope_subset)
x_attn = torch.index_add(
x,
dim=0,
source=self.ls1(residual_1),
index=indices_1,
alpha=residual_scale_factor,
)
indices_2 = (torch.randperm(b, device=x.device))[:sample_subset_size]
x_subset_2 = x_attn[indices_2]
residual_2 = self.mlp(self.norm2(x_subset_2))
x_ffn = torch.index_add(
x_attn,
dim=0,
source=self.ls2(residual_2),
index=indices_2,
alpha=residual_scale_factor,
)
else:
x_attn = x + self.ls1(self.attn(self.norm1(x), rope=rope))
x_ffn = x_attn + self.ls2(self.mlp(self.norm2(x_attn)))
return x_ffn
def _forward_list(self, x_list: List[Tensor], rope_list=None) -> List[Tensor]:
"""
This list operator concatenates the tokens from the list of inputs together to save
on the elementwise operations. Torch-compile memory-planning allows hiding the overhead
related to concat ops.
"""
b_list = [x.shape[0] for x in x_list]
sample_subset_sizes = [max(int(b * (1 - self.sample_drop_ratio)), 1) for b in b_list]
residual_scale_factors = [b / sample_subset_size for b, sample_subset_size in zip(b_list, sample_subset_sizes)]
if self.training and self.sample_drop_ratio > 0.0:
indices_1_list = [
(torch.randperm(b, device=x.device))[:sample_subset_size]
for x, b, sample_subset_size in zip(x_list, b_list, sample_subset_sizes)
]
x_subset_1_list = [x[indices_1] for x, indices_1 in zip(x_list, indices_1_list)]
if rope_list is not None:
rope_subset_list = [
self._maybe_index_rope(rope, indices_1) for rope, indices_1 in zip(rope_list, indices_1_list)
]
else:
rope_subset_list = rope_list
flattened, shapes, num_tokens = cat_keep_shapes(x_subset_1_list)
norm1 = uncat_with_shapes(self.norm1(flattened), shapes, num_tokens)
residual_1_list = self.attn.forward_list(norm1, rope_list=rope_subset_list)
x_attn_list = [
torch.index_add(
x,
dim=0,
source=self.ls1(residual_1),
index=indices_1,
alpha=residual_scale_factor,
)
for x, residual_1, indices_1, residual_scale_factor in zip(
x_list, residual_1_list, indices_1_list, residual_scale_factors
)
]
indices_2_list = [
(torch.randperm(b, device=x.device))[:sample_subset_size]
for x, b, sample_subset_size in zip(x_list, b_list, sample_subset_sizes)
]
x_subset_2_list = [x[indices_2] for x, indices_2 in zip(x_attn_list, indices_2_list)]
flattened, shapes, num_tokens = cat_keep_shapes(x_subset_2_list)
norm2_flat = self.norm2(flattened)
norm2_list = uncat_with_shapes(norm2_flat, shapes, num_tokens)
residual_2_list = self.mlp.forward_list(norm2_list)
x_ffn = [
torch.index_add(
x_attn,
dim=0,
source=self.ls2(residual_2),
index=indices_2,
alpha=residual_scale_factor,
)
for x_attn, residual_2, indices_2, residual_scale_factor in zip(
x_attn_list, residual_2_list, indices_2_list, residual_scale_factors
)
]
else:
x_out = []
for x, rope in zip(x_list, rope_list):
x_attn = x + self.ls1(self.attn(self.norm1(x), rope=rope))
x_ffn = x_attn + self.ls2(self.mlp(self.norm2(x_attn)))
x_out.append(x_ffn)
x_ffn = x_out
return x_ffn
def forward(self, x_or_x_list, rope_or_rope_list=None) -> List[Tensor]:
if isinstance(x_or_x_list, Tensor):
# for reference:
# return self._forward(x_or_x_list, rope=rope_or_rope_list)
# in order to match implementations we call the list op:
return self._forward_list([x_or_x_list], rope_list=[rope_or_rope_list])[0]
elif isinstance(x_or_x_list, list):
if rope_or_rope_list is None:
rope_or_rope_list = [None for x in x_or_x_list]
# return [self._forward(x, rope=rope) for x, rope in zip(x_or_x_list, rope_or_rope_list)]
return self._forward_list(x_or_x_list, rope_list=rope_or_rope_list)
else:
raise AssertionError
class CausalSelfAttentionBlock(nn.Module):
def __init__(
self,
dim: int,
num_heads: int,
ffn_ratio: float = 4.0,
ls_init_value: Optional[float] = None,
is_causal: bool = True,
act_layer: Callable = nn.GELU,
norm_layer: Callable = nn.LayerNorm,
dropout_prob: float = 0.0,
):
super().__init__()
self.dim = dim
self.is_causal = is_causal
self.ls1 = LayerScale(dim, init_values=ls_init_value) if ls_init_value else nn.Identity()
self.attention_norm = norm_layer(dim)
self.attention = CausalSelfAttention(dim, num_heads, attn_drop=dropout_prob, proj_drop=dropout_prob)
self.ffn_norm = norm_layer(dim)
ffn_hidden_dim = int(dim * ffn_ratio)
self.feed_forward = Mlp(
in_features=dim,
hidden_features=ffn_hidden_dim,
drop=dropout_prob,
act_layer=act_layer,
)
self.ls2 = LayerScale(dim, init_values=ls_init_value) if ls_init_value else nn.Identity()
def init_weights(
self,
init_attn_std: float | None = None,
init_proj_std: float | None = None,
init_fc_std: float | None = None,
factor: float = 1.0,
) -> None:
init_attn_std = init_attn_std or (self.dim**-0.5)
init_proj_std = init_proj_std or init_attn_std * factor
init_fc_std = init_fc_std or (2 * self.dim) ** -0.5
self.attention.init_weights(init_attn_std, init_proj_std)
self.attention_norm.reset_parameters()
nn.init.normal_(self.feed_forward.fc1.weight, std=init_fc_std)
nn.init.normal_(self.feed_forward.fc2.weight, std=init_proj_std)
self.ffn_norm.reset_parameters()
def forward(
self,
x: torch.Tensor,
):
x_attn = x + self.ls1(self.attention(self.attention_norm(x), self.is_causal))
x_ffn = x_attn + self.ls2(self.feed_forward(self.ffn_norm(x_attn)))
return x_ffn
|