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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/layers/patch_embed.py
import logging
from typing import Callable, List, Any, Tuple, Dict
import torch
from torch import nn, Tensor
from .attention import Attention, MemEffAttention
from .drop_path import DropPath
from .layer_scale import LayerScale
from .mlp import Mlp
logger = logging.getLogger("dinov2")
try:
from xformers.ops import fmha
from xformers.ops import scaled_index_add, index_select_cat
XFORMERS_AVAILABLE = True
except ImportError:
logger.warning("xFormers not available")
XFORMERS_AVAILABLE = False
class Block(nn.Module):
def __init__(
self,
dim: int,
num_heads: int,
mlp_ratio: float = 4.0,
qkv_bias: bool = False,
proj_bias: bool = True,
ffn_bias: bool = True,
drop: float = 0.0,
attn_drop: float = 0.0,
init_values=None,
drop_path: float = 0.0,
act_layer: Callable[..., nn.Module] = nn.GELU,
norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
attn_class: Callable[..., nn.Module] = Attention,
ffn_layer: Callable[..., nn.Module] = Mlp,
) -> None:
super().__init__()
# print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}")
self.norm1 = norm_layer(dim)
self.attn = attn_class(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
proj_bias=proj_bias,
attn_drop=attn_drop,
proj_drop=drop,
)
self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = ffn_layer(
in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=drop,
bias=ffn_bias,
)
self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.sample_drop_ratio = drop_path
def forward(self, x: Tensor) -> Tensor:
def attn_residual_func(x: Tensor) -> Tensor:
return self.ls1(self.attn(self.norm1(x)))
def ffn_residual_func(x: Tensor) -> Tensor:
return self.ls2(self.mlp(self.norm2(x)))
if self.training and self.sample_drop_ratio > 0.1:
# the overhead is compensated only for a drop path rate larger than 0.1
x = drop_add_residual_stochastic_depth(
x,
residual_func=attn_residual_func,
sample_drop_ratio=self.sample_drop_ratio,
)
x = drop_add_residual_stochastic_depth(
x,
residual_func=ffn_residual_func,
sample_drop_ratio=self.sample_drop_ratio,
)
elif self.training and self.sample_drop_ratio > 0.0:
x = x + self.drop_path1(attn_residual_func(x))
x = x + self.drop_path1(ffn_residual_func(x)) # FIXME: drop_path2
else:
x = x + attn_residual_func(x)
x = x + ffn_residual_func(x)
return x
def drop_add_residual_stochastic_depth(
x: Tensor,
residual_func: Callable[[Tensor], Tensor],
sample_drop_ratio: float = 0.0,
) -> Tensor:
# 1) extract subset using permutation
b, n, d = x.shape
sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
x_subset = x[brange]
# 2) apply residual_func to get residual
residual = residual_func(x_subset)
x_flat = x.flatten(1)
residual = residual.flatten(1)
residual_scale_factor = b / sample_subset_size
# 3) add the residual
x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
return x_plus_residual.view_as(x)
def get_branges_scales(x, sample_drop_ratio=0.0):
b, n, d = x.shape
sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
residual_scale_factor = b / sample_subset_size
return brange, residual_scale_factor
def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None):
if scaling_vector is None:
x_flat = x.flatten(1)
residual = residual.flatten(1)
x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
else:
x_plus_residual = scaled_index_add(
x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor
)
return x_plus_residual
attn_bias_cache: Dict[Tuple, Any] = {}
def get_attn_bias_and_cat(x_list, branges=None):
"""
this will perform the index select, cat the tensors, and provide the attn_bias from cache
"""
batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list]
all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list))
if all_shapes not in attn_bias_cache.keys():
seqlens = []
for b, x in zip(batch_sizes, x_list):
for _ in range(b):
seqlens.append(x.shape[1])
attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens)
attn_bias._batch_sizes = batch_sizes
attn_bias_cache[all_shapes] = attn_bias
if branges is not None:
cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1])
else:
tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list)
cat_tensors = torch.cat(tensors_bs1, dim=1)
return attn_bias_cache[all_shapes], cat_tensors
def drop_add_residual_stochastic_depth_list(
x_list: List[Tensor],
residual_func: Callable[[Tensor, Any], Tensor],
sample_drop_ratio: float = 0.0,
scaling_vector=None,
) -> Tensor:
# 1) generate random set of indices for dropping samples in the batch
branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list]
branges = [s[0] for s in branges_scales]
residual_scale_factors = [s[1] for s in branges_scales]
# 2) get attention bias and index+concat the tensors
attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges)
# 3) apply residual_func to get residual, and split the result
residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias)) # type: ignore
outputs = []
for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors):
outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x))
return outputs
class NestedTensorBlock(Block):
def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]:
"""
x_list contains a list of tensors to nest together and run
"""
assert isinstance(self.attn, MemEffAttention)
if self.training and self.sample_drop_ratio > 0.0:
def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
return self.attn(self.norm1(x), attn_bias=attn_bias)
def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
return self.mlp(self.norm2(x))
x_list = drop_add_residual_stochastic_depth_list(
x_list,
residual_func=attn_residual_func,
sample_drop_ratio=self.sample_drop_ratio,
scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None,
)
x_list = drop_add_residual_stochastic_depth_list(
x_list,
residual_func=ffn_residual_func,
sample_drop_ratio=self.sample_drop_ratio,
scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None,
)
return x_list
else:
def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias))
def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
return self.ls2(self.mlp(self.norm2(x)))
attn_bias, x = get_attn_bias_and_cat(x_list)
x = x + attn_residual_func(x, attn_bias=attn_bias)
x = x + ffn_residual_func(x)
return attn_bias.split(x)
def forward(self, x_or_x_list):
if isinstance(x_or_x_list, Tensor):
return super().forward(x_or_x_list)
elif isinstance(x_or_x_list, list):
assert XFORMERS_AVAILABLE, "Please install xFormers for nested tensors usage"
return self.forward_nested(x_or_x_list)
else:
raise AssertionError
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