File size: 6,298 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 | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This software may be used and distributed in accordance with
# the terms of the DINOv3 License Agreement.
import math
from typing import List, Tuple
import torch
import torch.nn.functional as F
from dinov3.utils import cat_keep_shapes, uncat_with_shapes
from torch import Tensor, nn
# RoPE-related functions:
def rope_rotate_half(x: Tensor) -> Tensor:
# x: [ x0 x1 x2 x3 x4 x5]
# out: [-x3 -x4 -x5 x0 x1 x2]
x1, x2 = x.chunk(2, dim=-1)
return torch.cat([-x2, x1], dim=-1)
def rope_apply(x: Tensor, sin: Tensor, cos: Tensor) -> Tensor:
# x: [..., D], eg [x0, x1, x2, x3, x4, x5]
# sin: [..., D], eg [sin0, sin1, sin2, sin0, sin1, sin2]
# cos: [..., D], eg [cos0, cos1, cos2, cos0, cos1, cos2]
return (x * cos) + (rope_rotate_half(x) * sin)
class LinearKMaskedBias(nn.Linear):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
o = self.out_features
assert o % 3 == 0
if self.bias is not None:
self.register_buffer("bias_mask", torch.full_like(self.bias, fill_value=math.nan))
def forward(self, input: Tensor) -> Tensor:
masked_bias = self.bias * self.bias_mask.to(self.bias.dtype) if self.bias is not None else None
return F.linear(input, self.weight, masked_bias)
class SelfAttention(nn.Module):
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
proj_bias: bool = True,
attn_drop: float = 0.0,
proj_drop: float = 0.0,
mask_k_bias: bool = False,
device=None,
) -> None:
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim**-0.5
linear_class = LinearKMaskedBias if mask_k_bias else nn.Linear
self.qkv = linear_class(dim, dim * 3, bias=qkv_bias, device=device)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim, bias=proj_bias, device=device)
self.proj_drop = nn.Dropout(proj_drop)
def apply_rope(self, q: Tensor, k: Tensor, rope: Tensor | Tuple[Tensor, Tensor]) -> Tuple[Tensor, Tensor]:
# All operations will use the dtype of rope, the output is cast back to the dtype of q and k
q_dtype = q.dtype
k_dtype = k.dtype
sin, cos = rope
rope_dtype = sin.dtype
q = q.to(dtype=rope_dtype)
k = k.to(dtype=rope_dtype)
N = q.shape[-2]
prefix = N - sin.shape[-2]
assert prefix >= 0
q_prefix = q[:, :, :prefix, :]
q = rope_apply(q[:, :, prefix:, :], sin, cos) # [B, head, hw, D//head]
q = torch.cat((q_prefix, q), dim=-2) # [B, head, N, D//head]
k_prefix = k[:, :, :prefix, :]
k = rope_apply(k[:, :, prefix:, :], sin, cos) # [B, head, hw, D//head]
k = torch.cat((k_prefix, k), dim=-2) # [B, head, N, D//head]
q = q.to(dtype=q_dtype)
k = k.to(dtype=k_dtype)
return q, k
def forward(self, x: Tensor, attn_bias=None, rope: Tensor = None) -> Tensor:
qkv = self.qkv(x)
attn_v = self.compute_attention(qkv=qkv, attn_bias=attn_bias, rope=rope)
x = self.proj(attn_v)
x = self.proj_drop(x)
return x
def forward_list(self, x_list, attn_bias=None, rope_list=None) -> List[Tensor]:
assert len(x_list) == len(rope_list) # should be enforced by the Block
x_flat, shapes, num_tokens = cat_keep_shapes(x_list)
qkv_flat = self.qkv(x_flat)
qkv_list = uncat_with_shapes(qkv_flat, shapes, num_tokens)
att_out = []
for _, (qkv, _, rope) in enumerate(zip(qkv_list, shapes, rope_list)):
att_out.append(self.compute_attention(qkv, attn_bias=attn_bias, rope=rope))
x_flat, shapes, num_tokens = cat_keep_shapes(att_out)
x_flat = self.proj(x_flat)
return uncat_with_shapes(x_flat, shapes, num_tokens)
def compute_attention(self, qkv: Tensor, attn_bias=None, rope=None) -> Tensor:
assert attn_bias is None
B, N, _ = qkv.shape
C = self.qkv.in_features
qkv = qkv.reshape(B, N, 3, self.num_heads, C // self.num_heads)
q, k, v = torch.unbind(qkv, 2)
q, k, v = [t.transpose(1, 2) for t in [q, k, v]]
if rope is not None:
q, k = self.apply_rope(q, k, rope)
x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
x = x.transpose(1, 2)
return x.reshape([B, N, C])
class CausalSelfAttention(nn.Module):
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
proj_bias: bool = True,
attn_drop: float = 0.0,
proj_drop: float = 0.0,
) -> None:
super().__init__()
self.dim = dim
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim**-0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = attn_drop
self.proj = nn.Linear(dim, dim, bias=proj_bias)
self.proj_drop = nn.Dropout(proj_drop)
def init_weights(
self, init_attn_std: float | None = None, init_proj_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
nn.init.normal_(self.qkv.weight, std=init_attn_std)
nn.init.normal_(self.proj.weight, std=init_proj_std)
if self.qkv.bias is not None:
nn.init.zeros_(self.qkv.bias)
if self.proj.bias is not None:
nn.init.zeros_(self.proj.bias)
def forward(self, x: Tensor, is_causal: bool = True) -> Tensor:
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
q, k, v = torch.unbind(qkv, 2)
q, k, v = [t.transpose(1, 2) for t in [q, k, v]]
x = torch.nn.functional.scaled_dot_product_attention(
q, k, v, attn_mask=None, dropout_p=self.attn_drop if self.training else 0, is_causal=is_causal
)
x = x.transpose(1, 2).contiguous().view(B, N, C)
x = self.proj_drop(self.proj(x))
return x
|