# 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