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# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the Apache License, Version 2.0
# 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 os
import warnings
from torch import Tensor
from torch import nn
XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
try:
if XFORMERS_ENABLED:
from xformers.ops import memory_efficient_attention, unbind
XFORMERS_AVAILABLE = True
warnings.warn("xFormers is available (Attention)")
else:
warnings.warn("xFormers is disabled (Attention)")
raise ImportError
except ImportError:
XFORMERS_AVAILABLE = False
warnings.warn("xFormers is not available (Attention)")
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,
) -> None:
super().__init__()
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 = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim, bias=proj_bias)
self.proj_drop = nn.Dropout(proj_drop)
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
class MemEffAttention(Attention):
def forward(self, x: Tensor, attn_bias=None) -> Tensor:
if not XFORMERS_AVAILABLE:
if attn_bias is not None:
raise AssertionError("xFormers is required for using nested tensors")
return super().forward(x)
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
q, k, v = unbind(qkv, 2)
x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
x = x.reshape([B, N, C])
x = self.proj(x)
x = self.proj_drop(x)
return x
class CrossAttention(nn.Module):
def __init__(
self,
dim: int,
dim_q: int,
dim_k: int,
dim_v: 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.to_q = nn.Linear(dim_q, dim, bias=qkv_bias)
self.to_k = nn.Linear(dim_k, dim, bias=qkv_bias)
self.to_v = nn.Linear(dim_v, dim, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim, bias=proj_bias)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
# q: [B, N, Cq]
# k: [B, M, Ck]
# v: [B, M, Cv]
# return: [B, N, C]
B, N, _ = q.shape
M = k.shape[1]
q = self.scale * self.to_q(q).reshape(B, N, self.num_heads, self.dim // self.num_heads).permute(0, 2, 1, 3) # [B, nh, N, C/nh]
k = self.to_k(k).reshape(B, M, self.num_heads, self.dim // self.num_heads).permute(0, 2, 1, 3) # [B, nh, M, C/nh]
v = self.to_v(v).reshape(B, M, self.num_heads, self.dim // self.num_heads).permute(0, 2, 1, 3) # [B, nh, M, C/nh]
attn = q @ k.transpose(-2, -1) # [B, nh, N, M]
attn = attn.softmax(dim=-1) # [B, nh, N, M]
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, -1) # [B, nh, N, M] @ [B, nh, M, C/nh] --> [B, nh, N, C/nh] --> [B, N, nh, C/nh] --> [B, N, C]
x = self.proj(x)
x = self.proj_drop(x)
return x
class MemEffCrossAttention(CrossAttention):
def forward(self, q: Tensor, k: Tensor, v: Tensor, attn_bias=None) -> Tensor:
if not XFORMERS_AVAILABLE:
if attn_bias is not None:
raise AssertionError("xFormers is required for using nested tensors")
return super().forward(x)
B, N, _ = q.shape
M = k.shape[1]
q = self.scale * self.to_q(q).reshape(B, N, self.num_heads, self.dim // self.num_heads) # [B, N, nh, C/nh]
k = self.to_k(k).reshape(B, M, self.num_heads, self.dim // self.num_heads) # [B, M, nh, C/nh]
v = self.to_v(v).reshape(B, M, self.num_heads, self.dim // self.num_heads) # [B, M, nh, C/nh]
x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
x = x.reshape(B, N, -1)
x = self.proj(x)
x = self.proj_drop(x)
return x
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