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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.
from functools import partial
import logging
import torch
from torch import nn
from dinov2.loss import DINOLoss, iBOTPatchLoss, KoLeoLoss
from dinov2.models import build_model_from_cfg
from dinov2.layers import DINOHead
from dinov2.utils.utils import has_batchnorms
from dinov2.utils.param_groups import get_params_groups_with_decay, fuse_params_groups
from dinov2.fsdp import get_fsdp_wrapper, ShardedGradScaler, get_fsdp_modules, reshard_fsdp_model
from dinov2.models.vision_transformer import BlockChunk
try:
from xformers.ops import fmha
XFORMERS_AVAILABLE = True
except ImportError:
XFORMERS_AVAILABLE = False
assert XFORMERS_AVAILABLE, "xFormers is required for DINOv2 training"
logger = logging.getLogger("dinov2")
class SSLMetaArch(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.fp16_scaler = ShardedGradScaler() if cfg.compute_precision.grad_scaler else None
student_model_dict = dict()
teacher_model_dict = dict()
student_backbone, teacher_backbone, embed_dim = build_model_from_cfg(cfg)
student_model_dict["backbone"] = student_backbone
teacher_model_dict["backbone"] = teacher_backbone
logger.info(f"OPTIONS -- architecture : embed_dim: {embed_dim}")
if cfg.student.pretrained_weights:
chkpt = torch.load(cfg.student.pretrained_weights)
logger.info(f"OPTIONS -- pretrained weights: loading from {cfg.student.pretrained_weights}")
student_backbone.load_state_dict(chkpt["model"], strict=False)
self.embed_dim = embed_dim
self.dino_out_dim = cfg.dino.head_n_prototypes
self.do_dino = cfg.dino.loss_weight > 0
self.do_koleo = cfg.dino.koleo_loss_weight > 0
self.do_ibot = cfg.ibot.loss_weight > 0
self.ibot_separate_head = cfg.ibot.separate_head
logger.info("OPTIONS -- DINO")
if self.do_dino:
logger.info(f"OPTIONS -- DINO -- loss_weight: {cfg.dino.loss_weight}")
logger.info(f"OPTIONS -- DINO -- head_n_prototypes: {cfg.dino.head_n_prototypes}")
logger.info(f"OPTIONS -- DINO -- head_bottleneck_dim: {cfg.dino.head_bottleneck_dim}")
logger.info(f"OPTIONS -- DINO -- head_hidden_dim: {cfg.dino.head_hidden_dim}")
self.dino_loss_weight = cfg.dino.loss_weight
dino_head = partial(
DINOHead,
in_dim=embed_dim,
out_dim=cfg.dino.head_n_prototypes,
hidden_dim=cfg.dino.head_hidden_dim,
bottleneck_dim=cfg.dino.head_bottleneck_dim,
nlayers=cfg.dino.head_nlayers,
)
self.dino_loss = DINOLoss(self.dino_out_dim)
if self.do_koleo:
logger.info("OPTIONS -- DINO -- applying KOLEO regularization")
self.koleo_loss = KoLeoLoss()
else:
logger.info("OPTIONS -- DINO -- not using DINO")
if self.do_dino or self.do_ibot:
student_model_dict["dino_head"] = dino_head()
teacher_model_dict["dino_head"] = dino_head()
logger.info("OPTIONS -- IBOT")
logger.info(f"OPTIONS -- IBOT -- loss_weight: {cfg.ibot.loss_weight}")
logger.info(f"OPTIONS -- IBOT masking -- ibot_mask_ratio_tuple: {cfg.ibot.mask_ratio_min_max}")
logger.info(f"OPTIONS -- IBOT masking -- ibot_mask_sample_probability: {cfg.ibot.mask_sample_probability}")
if self.do_ibot:
self.ibot_loss_weight = cfg.ibot.loss_weight
assert max(cfg.ibot.mask_ratio_min_max) > 0, "please provide a positive mask ratio tuple for ibot"
assert cfg.ibot.mask_sample_probability > 0, "please provide a positive mask probability for ibot"
self.ibot_out_dim = cfg.ibot.head_n_prototypes if self.ibot_separate_head else cfg.dino.head_n_prototypes
self.ibot_patch_loss = iBOTPatchLoss(self.ibot_out_dim)
if self.ibot_separate_head:
logger.info(f"OPTIONS -- IBOT -- loss_weight: {cfg.ibot.loss_weight}")
logger.info(f"OPTIONS -- IBOT -- head_n_prototypes: {cfg.ibot.head_n_prototypes}")
logger.info(f"OPTIONS -- IBOT -- head_bottleneck_dim: {cfg.ibot.head_bottleneck_dim}")
logger.info(f"OPTIONS -- IBOT -- head_hidden_dim: {cfg.ibot.head_hidden_dim}")
ibot_head = partial(
DINOHead,
in_dim=embed_dim,
out_dim=cfg.ibot.head_n_prototypes,
hidden_dim=cfg.ibot.head_hidden_dim,
bottleneck_dim=cfg.ibot.head_bottleneck_dim,
nlayers=cfg.ibot.head_nlayers,
)
student_model_dict["ibot_head"] = ibot_head()
teacher_model_dict["ibot_head"] = ibot_head()
else:
logger.info("OPTIONS -- IBOT -- head shared with DINO")
self.need_to_synchronize_fsdp_streams = True
self.student = nn.ModuleDict(student_model_dict)
self.teacher = nn.ModuleDict(teacher_model_dict)
# there is no backpropagation through the teacher, so no need for gradients
for p in self.teacher.parameters():
p.requires_grad = False
logger.info(f"Student and Teacher are built: they are both {cfg.student.arch} network.")
def forward(self, inputs):
raise NotImplementedError
def backprop_loss(self, loss):
if self.fp16_scaler is not None:
self.fp16_scaler.scale(loss).backward()
else:
loss.backward()
def forward_backward(self, images, teacher_temp):
n_global_crops = 2
assert n_global_crops == 2
n_local_crops = self.cfg.crops.local_crops_number
global_crops = images["collated_global_crops"].cuda(non_blocking=True)
local_crops = images["collated_local_crops"].cuda(non_blocking=True)
masks = images["collated_masks"].cuda(non_blocking=True)
mask_indices_list = images["mask_indices_list"].cuda(non_blocking=True)
n_masked_patches_tensor = images["n_masked_patches"].cuda(non_blocking=True)
n_masked_patches = mask_indices_list.shape[0]
upperbound = images["upperbound"]
masks_weight = images["masks_weight"].cuda(non_blocking=True)
n_local_crops_loss_terms = max(n_local_crops * n_global_crops, 1)
n_global_crops_loss_terms = (n_global_crops - 1) * n_global_crops
do_dino = self.do_dino
do_ibot = self.do_ibot
# loss scales
ibot_loss_scale = 1.0 / n_global_crops
# teacher output
@torch.no_grad()
def get_teacher_output():
x, n_global_crops_teacher = global_crops, n_global_crops
teacher_backbone_output_dict = self.teacher.backbone(x, is_training=True)
teacher_cls_tokens = teacher_backbone_output_dict["x_norm_clstoken"]
teacher_cls_tokens = teacher_cls_tokens.chunk(n_global_crops_teacher)
# watch out: these are chunked and cat'd in reverse so A is matched to B in the global crops dino loss
teacher_cls_tokens = torch.cat((teacher_cls_tokens[1], teacher_cls_tokens[0]))
ibot_teacher_patch_tokens = teacher_backbone_output_dict["x_norm_patchtokens"]
_dim = ibot_teacher_patch_tokens.shape[-1]
n_cls_tokens = teacher_cls_tokens.shape[0]
if do_ibot and not self.ibot_separate_head:
buffer_tensor_teacher = ibot_teacher_patch_tokens.new_zeros(upperbound + n_cls_tokens, _dim)
buffer_tensor_teacher[:n_cls_tokens].copy_(teacher_cls_tokens)
torch.index_select(
ibot_teacher_patch_tokens.flatten(0, 1),
dim=0,
index=mask_indices_list,
out=buffer_tensor_teacher[n_cls_tokens : n_cls_tokens + n_masked_patches],
)
tokens_after_head = self.teacher.dino_head(buffer_tensor_teacher)
teacher_cls_tokens_after_head = tokens_after_head[:n_cls_tokens]
masked_teacher_patch_tokens_after_head = tokens_after_head[
n_cls_tokens : n_cls_tokens + n_masked_patches
]
elif do_ibot and self.ibot_separate_head:
buffer_tensor_teacher = ibot_teacher_patch_tokens.new_zeros(upperbound, _dim)
torch.index_select(
ibot_teacher_patch_tokens.flatten(0, 1),
dim=0,
index=mask_indices_list,
out=buffer_tensor_teacher[:n_masked_patches],
)
teacher_cls_tokens_after_head = self.teacher.dino_head(teacher_cls_tokens)
masked_teacher_patch_tokens_after_head = self.teacher.ibot_head(buffer_tensor_teacher)[
:n_masked_patches
]
else:
teacher_cls_tokens_after_head = self.teacher.dino_head(teacher_cls_tokens)
masked_teacher_ibot_softmaxed_centered = None
if self.cfg.train.centering == "centering":
teacher_dino_softmaxed_centered_list = self.dino_loss.softmax_center_teacher(
teacher_cls_tokens_after_head, teacher_temp=teacher_temp
).view(n_global_crops_teacher, -1, *teacher_cls_tokens_after_head.shape[1:])
self.dino_loss.update_center(teacher_cls_tokens_after_head)
if do_ibot:
masked_teacher_patch_tokens_after_head = masked_teacher_patch_tokens_after_head.unsqueeze(0)
masked_teacher_ibot_softmaxed_centered = self.ibot_patch_loss.softmax_center_teacher(
masked_teacher_patch_tokens_after_head[:, :n_masked_patches], teacher_temp=teacher_temp
)
masked_teacher_ibot_softmaxed_centered = masked_teacher_ibot_softmaxed_centered.squeeze(0)
self.ibot_patch_loss.update_center(masked_teacher_patch_tokens_after_head[:n_masked_patches])
elif self.cfg.train.centering == "sinkhorn_knopp":
teacher_dino_softmaxed_centered_list = self.dino_loss.sinkhorn_knopp_teacher(
teacher_cls_tokens_after_head, teacher_temp=teacher_temp
).view(n_global_crops_teacher, -1, *teacher_cls_tokens_after_head.shape[1:])
if do_ibot:
masked_teacher_ibot_softmaxed_centered = self.ibot_patch_loss.sinkhorn_knopp_teacher(
masked_teacher_patch_tokens_after_head,
teacher_temp=teacher_temp,
n_masked_patches_tensor=n_masked_patches_tensor,
)
else:
raise NotImplementedError
return teacher_dino_softmaxed_centered_list, masked_teacher_ibot_softmaxed_centered
teacher_dino_softmaxed_centered_list, masked_teacher_ibot_softmaxed_centered = get_teacher_output()
reshard_fsdp_model(self.teacher)
loss_dict = {}
loss_accumulator = 0 # for backprop
student_global_backbone_output_dict, student_local_backbone_output_dict = self.student.backbone(
[global_crops, local_crops], masks=[masks, None], is_training=True
)
inputs_for_student_head_list = []
# 1a: local crops cls tokens
student_local_cls_tokens = student_local_backbone_output_dict["x_norm_clstoken"]
inputs_for_student_head_list.append(student_local_cls_tokens.unsqueeze(0))
# 1b: global crops cls tokens
student_global_cls_tokens = student_global_backbone_output_dict["x_norm_clstoken"]
inputs_for_student_head_list.append(student_global_cls_tokens.unsqueeze(0))
# 1c: global crops patch tokens
if do_ibot:
_dim = student_global_backbone_output_dict["x_norm_clstoken"].shape[-1]
ibot_student_patch_tokens = student_global_backbone_output_dict["x_norm_patchtokens"]
buffer_tensor_patch_tokens = ibot_student_patch_tokens.new_zeros(upperbound, _dim)
buffer_tensor_patch_tokens[:n_masked_patches].copy_(
torch.index_select(ibot_student_patch_tokens.flatten(0, 1), dim=0, index=mask_indices_list)
)
if not self.ibot_separate_head:
inputs_for_student_head_list.append(buffer_tensor_patch_tokens.unsqueeze(0))
else:
student_global_masked_patch_tokens_after_head = self.student.ibot_head(buffer_tensor_patch_tokens)[
:n_masked_patches
]
# 2: run
_attn_bias, cat_inputs = fmha.BlockDiagonalMask.from_tensor_list(inputs_for_student_head_list)
outputs_list = _attn_bias.split(self.student.dino_head(cat_inputs))
# 3a: local crops cls tokens
student_local_cls_tokens_after_head = outputs_list.pop(0).squeeze(0)
# 3b: global crops cls tokens
student_global_cls_tokens_after_head = outputs_list.pop(0).squeeze(0)
# 3c: global crops patch tokens
if do_ibot and not self.ibot_separate_head:
student_global_masked_patch_tokens_after_head = outputs_list.pop(0).squeeze(0)[:n_masked_patches]
if n_local_crops > 0:
dino_local_crops_loss = self.dino_loss(
student_output_list=student_local_cls_tokens_after_head.chunk(n_local_crops),
teacher_out_softmaxed_centered_list=teacher_dino_softmaxed_centered_list,
) / (n_global_crops_loss_terms + n_local_crops_loss_terms)
# store for display
loss_dict["dino_local_crops_loss"] = dino_local_crops_loss
# accumulate loss
loss_accumulator += self.dino_loss_weight * dino_local_crops_loss
# process global crops
loss_scales = 2 # this is here since we process global crops together
if do_dino:
# compute loss
dino_global_crops_loss = (
self.dino_loss(
student_output_list=[student_global_cls_tokens_after_head],
teacher_out_softmaxed_centered_list=[
teacher_dino_softmaxed_centered_list.flatten(0, 1)
], # these were chunked and stacked in reverse so A is matched to B
)
* loss_scales
/ (n_global_crops_loss_terms + n_local_crops_loss_terms)
)
loss_dict["dino_global_crops_loss"] = dino_global_crops_loss
# accumulate loss
loss_accumulator += self.dino_loss_weight * dino_global_crops_loss
student_cls_tokens = student_global_cls_tokens
if self.do_koleo:
koleo_loss = self.cfg.dino.koleo_loss_weight * sum(
self.koleo_loss(p) for p in student_cls_tokens.chunk(2)
) # we don't apply koleo loss between cls tokens of a same image
loss_accumulator += koleo_loss
loss_dict["koleo_loss"] = (
koleo_loss / loss_scales
) # this is to display the same losses as before but we can remove eventually
if do_ibot:
# compute loss
ibot_patch_loss = (
self.ibot_patch_loss.forward_masked(
student_global_masked_patch_tokens_after_head,
masked_teacher_ibot_softmaxed_centered,
student_masks_flat=masks,
n_masked_patches=n_masked_patches,
masks_weight=masks_weight,
)
* loss_scales
* ibot_loss_scale
)
# store for display
loss_dict["ibot_loss"] = ibot_patch_loss / 2
# accumulate loss
loss_accumulator += self.ibot_loss_weight * ibot_patch_loss
self.backprop_loss(loss_accumulator)
self.fsdp_synchronize_streams()
return loss_dict
def fsdp_synchronize_streams(self):
if self.need_to_synchronize_fsdp_streams:
torch.cuda.synchronize()
self.student.dino_head._streams = (
self.teacher.dino_head._streams
) = self.student.backbone._streams = self.teacher.backbone._streams
self.need_to_synchronize_fsdp_streams = False
def update_teacher(self, m):
student_param_list = []
teacher_param_list = []
with torch.no_grad():
for k in self.student.keys():
for ms, mt in zip(get_fsdp_modules(self.student[k]), get_fsdp_modules(self.teacher[k])):
student_param_list += ms.params
teacher_param_list += mt.params
torch._foreach_mul_(teacher_param_list, m)
torch._foreach_add_(teacher_param_list, student_param_list, alpha=1 - m)
def train(self):
super().train()
self.teacher.eval()
def get_maybe_fused_params_for_submodel(self, m):
params_groups = get_params_groups_with_decay(
model=m,
lr_decay_rate=self.cfg.optim.layerwise_decay,
patch_embed_lr_mult=self.cfg.optim.patch_embed_lr_mult,
)
fused_params_groups = fuse_params_groups(params_groups)
logger.info("fusing param groups")
for g in fused_params_groups:
g["foreach"] = True
return fused_params_groups
def get_params_groups(self):
all_params_groups = []
for m in self.student.values():
all_params_groups += self.get_maybe_fused_params_for_submodel(m)
return all_params_groups
def prepare_for_distributed_training(self):
logger.info("DISTRIBUTED FSDP -- preparing model for distributed training")
if has_batchnorms(self.student):
raise NotImplementedError
# below will synchronize all student subnetworks across gpus:
for k, v in self.student.items():
self.teacher[k].load_state_dict(self.student[k].state_dict())
student_model_cfg = self.cfg.compute_precision.student[k]
self.student[k] = get_fsdp_wrapper(student_model_cfg, modules_to_wrap={BlockChunk})(self.student[k])
teacher_model_cfg = self.cfg.compute_precision.teacher[k]
self.teacher[k] = get_fsdp_wrapper(teacher_model_cfg, modules_to_wrap={BlockChunk})(self.teacher[k])
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