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# Copyright (c) 2023-2024, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
from logging import getLogger
import math
import os
from typing import Dict, List, Optional, 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, n_power_iterations=20).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, n_power_iterations: int = None) -> torch.Tensor:
if not n_power_iterations:
n_power_iterations = self.n_power_iterations
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, 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 _ChunkedSNReweight(nn.Module):
def __init__(self, weight: torch.Tensor, num_chunks: int, *args, init_norm_to_current: bool = False, **kwargs):
super().__init__()
self.num_chunks = num_chunks
parts = weight.split(weight.shape[0] // num_chunks, dim=0)
self.parts = nn.ModuleList([
_SNReweight(p, *args, init_norm_to_current=init_norm_to_current, **kwargs)
for p in parts
])
def forward(self, weight: torch.Tensor, *args, **kwargs):
parts = weight.split(weight.shape[0] // self.num_chunks, dim=0)
parts = [
fn(p)
for fn, p in zip(self.parts, parts)
]
return torch.cat(parts, dim=0)
class _AttnSNReweight(_ChunkedSNReweight):
def __init__(self, weight: torch.Tensor, *args, init_norm_to_current: bool = False, renorm_values: bool = False, **kwargs):
super().__init__(weight, 3, *args, init_norm_to_current=init_norm_to_current, **kwargs)
if not renorm_values:
self.parts[2] = nn.Identity()
def enable_spectral_reparam(model: Union[nn.Module, List[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,
state_dict_guidance: Optional[Dict[str, torch.Tensor]] = None):
if isinstance(model, (list, tuple)):
for i, sub in enumerate(model):
sub_sd = state_dict_guidance[i] if isinstance(state_dict_guidance, (list, tuple)) else state_dict_guidance
enable_spectral_reparam(sub, n_power_iterations=n_power_iterations, eps=eps,
init_norm_to_current=init_norm_to_current, renorm_values=renorm_values,
renorm_mlp=renorm_mlp, state_dict_guidance=sub_sd)
return
print('Enabling spectral reparametrization')
args = dict(n_power_iterations=n_power_iterations, dim=0, eps=eps, init_norm_to_current=init_norm_to_current)
visited_prefixes = set()
def is_guidance_parametrized(name: str):
if state_dict_guidance is None:
return True
p_name = f'{name}.parametrizations'
is_prm = any(k for k in state_dict_guidance if k.startswith(p_name))
return is_prm
def parametrize_linear(linear: nn.Linear):
parametrize.register_parametrization(
linear,
'weight',
_SNReweight(linear.weight, **args)
)
for name, mod in model.named_modules():
pref = '.'.join(name.split('.')[:-1])
if pref in visited_prefixes:
continue
if isinstance(mod, Attention) or name.endswith('.attn'):
if is_guidance_parametrized(f'{name}.qkv'):
parametrize.register_parametrization(
mod.qkv,
'weight',
_AttnSNReweight(mod.qkv.weight, renorm_values=renorm_values, **args),
)
if hasattr(mod, 'proj') and is_guidance_parametrized(f'{name}.proj'):
parametrize_linear(mod.proj)
visited_prefixes.add(name)
elif name.endswith('mlp') and renorm_mlp and hasattr(mod, 'w12'):
if is_guidance_parametrized(f'{name}.w12'):
parametrize.register_parametrization(
mod.w12,
'weight',
_ChunkedSNReweight(mod.w12.weight, num_chunks=2, **args),
)
if is_guidance_parametrized(f'{name}.w3'):
parametrize_linear(mod.w3)
visited_prefixes.add(name)
elif isinstance(mod, nn.Linear) and 'patch_generator' not in name and is_guidance_parametrized(name):
parametrize_linear(mod)
def configure_spectral_reparam_from_args(model: nn.Module, args, state_dict_guidance: Optional[Dict[str, torch.Tensor]] = None):
spectral_reparam = getattr(args, 'spectral_reparam', False)
if isinstance(spectral_reparam, bool) and spectral_reparam:
enable_spectral_reparam(model, init_norm_to_current=True, state_dict_guidance=state_dict_guidance)
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=True,
state_dict_guidance=state_dict_guidance,
)
def disable_spectral_reparam(model: nn.Module):
print('Disabling spectral reparametrization')
for name, mod in model.named_modules():
if parametrize.is_parametrized(mod):
parametrize.remove_parametrizations(mod, '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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