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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
import torch
from torch import nn
from torch.utils.checkpoint import checkpoint
from xformers.ops import memory_efficient_attention, unbind
class MemEffAttention(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,
gradient_checkpointing: bool = False,
) -> None:
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim**-0.5
self.gradient_checkpointing = gradient_checkpointing
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: torch.Tensor, attn_bias=None) -> torch.Tensor:
if self.training and self.gradient_checkpointing:
return checkpoint(self._forward, x, attn_bias, use_reentrant=False)
else:
return self._forward(x, attn_bias)
def _forward(self, x: torch.Tensor, attn_bias=None) -> torch.Tensor:
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 MemEffCrossAttention(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,
gradient_checkpointing: bool = False,
) -> None:
super().__init__()
self.dim = dim
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim**-0.5
self.gradient_checkpointing = gradient_checkpointing
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: torch.Tensor, k: torch.Tensor, v: torch.Tensor, attn_bias=None) -> torch.Tensor:
if self.training and self.gradient_checkpointing:
return checkpoint(self._forward, q, k, v, attn_bias, use_reentrant=False)
else:
return self._forward(q, k, v, attn_bias)
def _forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, attn_bias=None) -> torch.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) # [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 |