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#
# This software may be used and distributed in accordance with
# the terms of the DINOv3 License Agreement.
import logging
from collections import defaultdict
logger = logging.getLogger("dinov3")
def get_vit_lr_decay_rate(
name,
lr_decay_rate=1.0,
num_layers=12,
force_is_backbone=False,
chunked_blocks=False,
):
"""
Calculate lr decay rate for different ViT blocks.
Args:
name (string): parameter name.
lr_decay_rate (float): base lr decay rate.
num_layers (int): number of ViT blocks.
Returns:
lr decay rate for the given parameter.
"""
layer_id = num_layers + 1
if name.startswith("backbone") or force_is_backbone:
if (
".pos_embed" in name
or ".patch_embed" in name
or ".mask_token" in name
or ".cls_token" in name
or ".storage_tokens" in name
):
layer_id = 0
elif force_is_backbone and (
"pos_embed" in name
or "patch_embed" in name
or "mask_token" in name
or "cls_token" in name
or "storage_tokens" in name
):
layer_id = 0
elif ".blocks." in name and ".residual." not in name:
layer_id = int(name[name.find(".blocks.") :].split(".")[2]) + 1
elif chunked_blocks and "blocks." in name and "residual." not in name:
layer_id = int(name[name.find("blocks.") :].split(".")[2]) + 1
elif "blocks." in name and "residual." not in name:
layer_id = int(name[name.find("blocks.") :].split(".")[1]) + 1
return lr_decay_rate ** (num_layers + 1 - layer_id)
def get_params_groups_with_decay(model, lr_decay_rate=1.0, patch_embed_lr_mult=1.0, dino_head_wd_multiplier=1.0):
chunked_blocks = False
if hasattr(model, "n_blocks"):
logger.info("chunked fsdp")
n_blocks = model.n_blocks
chunked_blocks = model.chunked_blocks
elif hasattr(model, "blocks"):
logger.info("first code branch")
n_blocks = len(model.blocks)
elif hasattr(model, "backbone"):
logger.info("second code branch")
n_blocks = len(model.backbone.blocks)
else:
logger.info("else code branch")
n_blocks = 0
all_param_groups = []
for name, param in model.named_parameters():
name = remove_fsdp_compile_names(name)
if not param.requires_grad:
continue
decay_rate = get_vit_lr_decay_rate(
name,
lr_decay_rate,
num_layers=n_blocks,
force_is_backbone=n_blocks > 0,
chunked_blocks=chunked_blocks,
)
d = {
"name": name,
"params": param,
"is_last_layer": False,
"lr_multiplier": decay_rate,
"wd_multiplier": 1.0,
}
if "dino_head" in name:
d["wd_multiplier"] = dino_head_wd_multiplier
if "last_layer" in name:
d["is_last_layer"] = True
# No weight-decay on biases, norm parameters, layer scale gamma, learned tokens and embeddings
if name.endswith("bias") or "norm" in name or "gamma" in name or "fourier_w" in name:
d["wd_multiplier"] = 0.0
if "patch_embed" in name:
d["lr_multiplier"] *= patch_embed_lr_mult
all_param_groups.append(d)
logger.info(f"{name}: lr_multiplier: {d['lr_multiplier']}, wd_multiplier: {d['wd_multiplier']}")
return all_param_groups
def fuse_params_groups(all_params_groups, keys=("lr_multiplier", "wd_multiplier", "is_last_layer")):
fused_params_groups = defaultdict(lambda: {"params": []})
for d in all_params_groups:
identifier = ""
for k in keys:
identifier += k + str(d[k]) + "_"
for k in keys:
fused_params_groups[identifier][k] = d[k]
fused_params_groups[identifier]["params"].append(d["params"])
return fused_params_groups.values()
def get_params_groups_with_decay_fsdp(model, lr_decay_rate=1.0, patch_embed_lr_mult=1.0, dino_head_wd_multiplier=1.0):
if hasattr(model, "module"): # SimpleFSDP
is_backbone = hasattr(model.module, "blocks")
n_blocks = len(model.module.blocks) if is_backbone else 0
else: # FSDP2
is_backbone = hasattr(model, "blocks")
n_blocks = len(model.blocks) if is_backbone else 0
all_param_groups = []
for name, param in model.named_parameters():
name = remove_fsdp_compile_names(name)
if not param.requires_grad:
continue
decay_rate = get_vit_lr_decay_rate(
name,
lr_decay_rate,
num_layers=n_blocks,
force_is_backbone=n_blocks > 0,
chunked_blocks=False,
)
d = {
"name": name,
"params": param,
"is_last_layer": False,
"lr_multiplier": decay_rate,
"wd_multiplier": 1.0,
}
if "dino_head" in name:
d["wd_multiplier"] = dino_head_wd_multiplier
if "last_layer" in name:
d["is_last_layer"] = True
# No weight-decay on biases, norm parameters, layer scale gamma, learned tokens and embeddings
if name.endswith("bias") or "norm" in name or "gamma" in name or "fourier_w" in name:
d["wd_multiplier"] = 0.0
if "patch_embed" in name:
d["lr_multiplier"] *= patch_embed_lr_mult
all_param_groups.append(d)
logger.info(f"{name}: lr_multiplier: {d['lr_multiplier']}, wd_multiplier: {d['wd_multiplier']}")
return all_param_groups
def remove_fsdp_compile_names(name: str):
name = name.replace("_fsdp_wrapped_module.", "") # Added by FSDP
name = name.replace("_checkpoint_wrapped_module.", "") # Added by activation checkpointing for xFSDP
name = name.replace("parametrizations.", "") # Added by xFSDP
name = name.removesuffix(".original") # Added by xFSDP
name = name.replace("module.", "") # Added by xFSDP
name = name.replace("_orig_mod.", "") # Added by torch.compile
return name
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