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from logging import getLogger
import math
import os
from typing import Union, Tuple
from types import MethodType
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.utils import parametrize
from torch.nn.utils.parametrizations import _SpectralNorm
from timm.models.vision_transformer import Attention, Mlp
_EPS = 1e-5
class _SNReweight(_SpectralNorm):
def __init__(self, weight: torch.Tensor, *args, init_norm_to_current: bool = False, alpha: float = 0.05, version: int = 2, **kwargs):
super().__init__(weight, *args, **kwargs)
self.alpha = alpha
self.version = version
self.register_buffer('_sn_version', torch.tensor(version))
if init_norm_to_current:
# This will set the numerator to match the denominator, which should preserve the original values
init_scale = self._get_sigma(weight).item()
else:
init_scale = 1.0
if version == 1:
init_value = init_scale
elif version == 2:
t = init_scale - alpha
if t < _EPS:
getLogger("spectral_reparam").warn(f'The initialized spectral norm {init_scale} is too small to be represented. Setting to {_EPS} instead.')
t = _EPS
init_value = math.log(math.exp(t) - 1)
else:
raise ValueError(f'Unsupported version: {version}')
# Make 2D so that weight decay gets applied
self.scale = nn.Parameter(torch.tensor([[init_value]], dtype=torch.float32, device=weight.device))
# Re-implementing this because we need to make division by sigma safe
def _get_sigma(self, weight: torch.Tensor) -> torch.Tensor:
if weight.ndim == 1:
# Faster and more exact path, no need to approximate anything
sigma = weight.norm()
else:
weight_mat = self._reshape_weight_to_matrix(weight)
if self.training:
self._power_method(weight_mat, self.n_power_iterations)
# See above on why we need to clone
u = self._u.clone(memory_format=torch.contiguous_format)
v = self._v.clone(memory_format=torch.contiguous_format)
# The proper way of computing this should be through F.bilinear, but
# it seems to have some efficiency issues:
# https://github.com/pytorch/pytorch/issues/58093
sigma = torch.dot(u, torch.mv(weight_mat, v))
return sigma + self.eps
def forward(self, weight: torch.Tensor, *args, **kwargs):
dtype = weight.dtype
sigma = self._get_sigma(weight, *args, **kwargs)
if self.version == 1:
scale = self.scale
elif self.version == 2:
scale = F.softplus(self.scale) + self.alpha
else:
raise ValueError(f'Unsupported version: {self.version}')
scale = scale.float() / sigma.float()
y = weight * scale
if dtype in (torch.float16, torch.bfloat16):
y = y.to(dtype)
return y
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
version_key = f'{prefix}_sn_version'
if version_key not in state_dict:
self.version = 1
state_dict[version_key] = torch.tensor(1)
return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
class _AttnSNReweight(nn.Module):
def __init__(self, weight: torch.Tensor, *args, init_norm_to_current: bool = False, renorm_values: bool = False, **kwargs):
super().__init__()
parts = weight.split(weight.shape[0] // 3, dim=0)
ct = 2 if not renorm_values else 3
self.parts = nn.ModuleList([
_SNReweight(p, *args, init_norm_to_current=init_norm_to_current, **kwargs) if i < ct else nn.Identity()
for i, p in enumerate(parts)
])
def forward(self, weight: torch.Tensor, *args, **kwargs):
parts = weight.split(weight.shape[0] // 3, dim=0)
parts = [
fn(p)
for fn, p in zip(self.parts, parts)
]
return torch.cat(parts, dim=0)
def enable_spectral_reparam(model: nn.Module,
n_power_iterations: int = 1,
eps: float = 1e-6,
init_norm_to_current: bool = False,
renorm_values: bool = True,
renorm_mlp: bool = True):
# print('Enabling spectral reparametrization')
for mod in model.modules():
if isinstance(mod, Attention):
parametrize.register_parametrization(
mod.qkv,
'weight',
_AttnSNReweight(mod.qkv.weight, n_power_iterations, dim=0, eps=eps, init_norm_to_current=init_norm_to_current, renorm_values=renorm_values),
)
pass
elif isinstance(mod, Mlp) and renorm_mlp:
parametrize.register_parametrization(
mod.fc1,
'weight',
_SNReweight(mod.fc1.weight, n_power_iterations, dim=0, eps=eps, init_norm_to_current=init_norm_to_current),
)
parametrize.register_parametrization(
mod.fc2,
'weight',
_SNReweight(mod.fc2.weight, n_power_iterations, dim=0, eps=eps, init_norm_to_current=init_norm_to_current),
)
pass
def configure_spectral_reparam_from_args(model: nn.Module, args):
spectral_reparam = getattr(args, 'spectral_reparam', False)
if isinstance(spectral_reparam, bool) and spectral_reparam:
enable_spectral_reparam(model, init_norm_to_current=args.pretrained)
elif isinstance(spectral_reparam, dict):
enable_spectral_reparam(
model,
n_power_iterations=spectral_reparam.get('n_power_iterations', 1),
eps=spectral_reparam.get('eps', 1e-12),
init_norm_to_current=args.pretrained,
)
def disable_spectral_reparam(model: nn.Module):
for mod in model.modules():
if isinstance(mod, Attention):
parametrize.remove_parametrizations(mod.qkv, 'weight')
pass
elif isinstance(mod, Mlp):
parametrize.remove_parametrizations(mod.fc1, 'weight')
parametrize.remove_parametrizations(mod.fc2, 'weight')
pass
if __name__ == '__main__':
import argparse
from . import radio_model as create_model
parser = argparse.ArgumentParser(description='Remove parametrization from state dict')
parser.add_argument('--checkpoint', type=str, required=True, help='The checkpoint to load')
parser.add_argument('--output', type=str, default='', help='Where to store the checkpoint')
parser.add_argument('--release', default=False, action='store_true', help='Prune extraneous checkpoint fields')
parser.add_argument('--strict', default=False, action='store_true', help='Strictly load the state dict')
args = parser.parse_args()
if not args.output:
chk_dir, chk_name = os.path.split(args.checkpoint)
args.output = os.path.join(chk_dir, f'clean_{chk_name}')
print(f'Set output to "{args.output}"')
chk = torch.load(args.checkpoint, map_location='cpu', mmap=True)
model = create_model.create_model_from_args(chk['args'])
key = 'base_model.'
mod_state = dict()
extra_state = dict()
for k, v in chk['state_dict'].items():
if k.startswith(key):
mod_state[k[len(key):]] = v
else:
extra_state[k] = v
chk_load_info = model.load_state_dict(mod_state, strict=args.strict)
if chk_load_info.unexpected_keys or chk_load_info.missing_keys:
print(chk_load_info)
if chk['args'].spectral_reparam:
disable_spectral_reparam(model)
if hasattr(chk['args'], 'dtype'):
model.to(dtype=chk['args'].dtype)
mod_state = model.state_dict()
final_state = dict()
final_state.update({f'{key}{k}': v for k, v in mod_state.items()})
final_state.update(extra_state)
chk['state_dict'] = final_state
chk['args'].spectral_reparam = False
if args.release:
chk = {
'arch': chk['arch'],
'epoch': chk['epoch'],
'state_dict': chk['state_dict'],
'args': chk['args'],
}
torch.save(chk, args.output)
pass
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