94 lines
3.0 KiB
Python
94 lines
3.0 KiB
Python
# 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 logging
|
|
import os
|
|
import warnings
|
|
|
|
from torch import Tensor
|
|
from torch import nn
|
|
import torch.nn.functional as F
|
|
|
|
XFORMERS_AVAILABLE = False
|
|
|
|
|
|
class Attention(nn.Module):
|
|
def __init__(
|
|
self,
|
|
dim: int,
|
|
num_heads: int = 8,
|
|
qkv_bias: bool = True,
|
|
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
|
|
self.head_dim = dim // num_heads
|
|
self.scale = self.head_dim**-0.5
|
|
self.fused_attn = fused_attn
|
|
|
|
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
|
self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
|
|
self.k_norm = norm_layer(self.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) -> Tensor:
|
|
B, N, C = x.shape
|
|
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
|
|
q, k, v = qkv.unbind(0)
|
|
q, k = self.q_norm(q), self.k_norm(k)
|
|
|
|
if self.rope 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)
|
|
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
|
|
|
|
|
|
class MemEffAttention(Attention):
|
|
def forward(self, x: Tensor, attn_bias=None, pos=None) -> Tensor:
|
|
assert pos is None
|
|
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
|