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on
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Running
on
Zero
File size: 3,264 Bytes
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# References:
# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
import logging
import torch.nn.functional as F
from torch import Tensor, nn
logger = logging.getLogger("dinov2")
class Attention(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,
norm_layer: nn.Module = nn.LayerNorm,
qk_norm: bool = False,
fused_attn: bool = True, # use F.scaled_dot_product_attention or not
rope=None,
) -> None:
super().__init__()
assert dim % num_heads == 0, "dim should be divisible by num_heads"
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim**-0.5
self.fused_attn = fused_attn
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.q_norm = norm_layer(head_dim) if qk_norm else nn.Identity()
self.k_norm = norm_layer(head_dim) if qk_norm else nn.Identity()
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim, bias=proj_bias)
self.proj_drop = nn.Dropout(proj_drop)
self.rope = rope
def forward(self, x: Tensor, pos=None, attn_mask=None) -> Tensor:
B, N, C = x.shape
qkv = (
self.qkv(x)
.reshape(B, N, 3, self.num_heads, C // self.num_heads)
.permute(2, 0, 3, 1, 4)
)
q, k, v = qkv[0], qkv[1], qkv[2]
q, k = self.q_norm(q), self.k_norm(k)
if self.rope is not None and pos is not None:
q = self.rope(q, pos)
k = self.rope(k, pos)
if self.fused_attn:
x = F.scaled_dot_product_attention(
q,
k,
v,
dropout_p=self.attn_drop.p if self.training else 0.0,
attn_mask=(
(attn_mask)[:, None].repeat(1, self.num_heads, 1, 1)
if attn_mask is not None
else None
),
)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
def _forward(self, x: Tensor) -> Tensor:
B, N, C = x.shape
qkv = (
self.qkv(x)
.reshape(B, N, 3, self.num_heads, C // self.num_heads)
.permute(2, 0, 3, 1, 4)
)
q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
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