Spaces:
Paused
Paused
File size: 9,130 Bytes
9235b7f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 |
# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
import torch
from PIL.Image import Image
from collections import OrderedDict
from scepter.modules.utils.distribute import we
from scepter.modules.utils.config import Config
from scepter.modules.utils.logger import get_logger
from scepter.studio.utils.env import get_available_memory
from scepter.modules.model.registry import MODELS, BACKBONES, EMBEDDERS
from scepter.modules.utils.registry import Registry, build_from_config
def get_model(model_tuple):
assert 'model' in model_tuple
return model_tuple['model']
class BaseInference():
'''
support to load the components dynamicly.
create and load model when run this model at the first time.
'''
def __init__(self, cfg, logger=None):
if logger is None:
logger = get_logger(name='scepter')
self.logger = logger
self.name = cfg.NAME
def init_from_modules(self, modules):
for k, v in modules.items():
self.__setattr__(k, v)
def infer_model(self, cfg, module_paras=None):
module = {
'model': None,
'cfg': cfg,
'device': 'offline',
'name': cfg.NAME,
'function_info': {},
'paras': {}
}
if module_paras is None:
return module
function_info = {}
paras = {
k.lower(): v
for k, v in module_paras.get('PARAS', {}).items()
}
for function in module_paras.get('FUNCTION', []):
input_dict = {}
for inp in function.get('INPUT', []):
if inp.lower() in self.input:
input_dict[inp.lower()] = self.input[inp.lower()]
function_info[function.NAME] = {
'dtype': function.get('DTYPE', 'float32'),
'input': input_dict
}
module['paras'] = paras
module['function_info'] = function_info
return module
def init_from_ckpt(self, path, model, ignore_keys=list()):
if path.endswith('safetensors'):
from safetensors.torch import load_file as load_safetensors
sd = load_safetensors(path)
else:
sd = torch.load(path, map_location='cpu', weights_only=True)
new_sd = OrderedDict()
for k, v in sd.items():
ignored = False
for ik in ignore_keys:
if ik in k:
if we.rank == 0:
self.logger.info(
'Ignore key {} from state_dict.'.format(k))
ignored = True
break
if not ignored:
new_sd[k] = v
missing, unexpected = model.load_state_dict(new_sd, strict=False)
if we.rank == 0:
self.logger.info(
f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
)
if len(missing) > 0:
self.logger.info(f'Missing Keys:\n {missing}')
if len(unexpected) > 0:
self.logger.info(f'\nUnexpected Keys:\n {unexpected}')
def load(self, module):
if module['device'] == 'offline':
from scepter.modules.utils.import_utils import LazyImportModule
if (LazyImportModule.get_module_type(('MODELS', module['cfg'].NAME)) or
module['cfg'].NAME in MODELS.class_map):
model = MODELS.build(module['cfg'], logger=self.logger).eval()
elif (LazyImportModule.get_module_type(('BACKBONES', module['cfg'].NAME)) or
module['cfg'].NAME in BACKBONES.class_map):
model = BACKBONES.build(module['cfg'],
logger=self.logger).eval()
elif (LazyImportModule.get_module_type(('EMBEDDERS', module['cfg'].NAME)) or
module['cfg'].NAME in EMBEDDERS.class_map):
model = EMBEDDERS.build(module['cfg'],
logger=self.logger).eval()
else:
raise NotImplementedError
if 'DTYPE' in module['cfg'] and module['cfg']['DTYPE'] is not None:
model = model.to(getattr(torch, module['cfg'].DTYPE))
if module['cfg'].get('RELOAD_MODEL', None):
self.init_from_ckpt(module['cfg'].RELOAD_MODEL, model)
module['model'] = model
module['device'] = 'cpu'
if module['device'] == 'cpu':
module['device'] = we.device_id
module['model'] = module['model'].to(we.device_id)
return module
def unload(self, module):
if module is None:
return module
mem = get_available_memory()
free_mem = int(mem['available'] / (1024**2))
total_mem = int(mem['total'] / (1024**2))
if free_mem < 0.5 * total_mem:
if module['model'] is not None:
module['model'] = module['model'].to('cpu')
del module['model']
module['model'] = None
module['device'] = 'offline'
print('delete module')
else:
if module['model'] is not None:
module['model'] = module['model'].to('cpu')
module['device'] = 'cpu'
else:
module['device'] = 'offline'
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
return module
def dynamic_load(self, module=None, name=''):
self.logger.info('Loading {} model'.format(name))
if name == 'all':
for subname in self.loaded_model_name:
self.loaded_model[subname] = self.dynamic_load(
getattr(self, subname), subname)
elif name in self.loaded_model_name:
if name in self.loaded_model:
if module['cfg'] != self.loaded_model[name]['cfg']:
self.unload(self.loaded_model[name])
module = self.load(module)
self.loaded_model[name] = module
return module
elif module['device'] == 'cpu' or module['device'] == 'offline':
module = self.load(module)
return module
else:
return module
else:
module = self.load(module)
self.loaded_model[name] = module
return module
else:
return self.load(module)
def dynamic_unload(self, module=None, name='', skip_loaded=False):
self.logger.info('Unloading {} model'.format(name))
if name == 'all':
for name, module in self.loaded_model.items():
module = self.unload(self.loaded_model[name])
self.loaded_model[name] = module
elif name in self.loaded_model_name:
if name in self.loaded_model:
if not skip_loaded:
module = self.unload(self.loaded_model[name])
self.loaded_model[name] = module
else:
self.unload(module)
else:
self.unload(module)
def load_default(self, cfg):
module_paras = {}
if cfg is not None:
self.paras = cfg.PARAS
self.input_cfg = {k.lower(): v for k, v in cfg.INPUT.items()}
self.input = {k.lower(): dict(v).get('DEFAULT', None) if isinstance(v, (dict, OrderedDict, Config)) else v for k, v in cfg.INPUT.items()}
self.output = {k.lower(): v for k, v in cfg.OUTPUT.items()}
module_paras = cfg.MODULES_PARAS
return module_paras
def load_image(self, image, num_samples=1):
if isinstance(image, torch.Tensor):
pass
elif isinstance(image, Image):
pass
elif isinstance(image, Image):
pass
def get_function_info(self, module, function_name=None):
all_function = module['function_info']
if function_name in all_function:
return function_name, all_function[function_name]['dtype']
if function_name is None and len(all_function) == 1:
for k, v in all_function.items():
return k, v['dtype']
@torch.no_grad()
def __call__(self,
input,
**kwargs):
return
def build_inference(cfg, registry, logger=None, *args, **kwargs):
""" After build model, load pretrained model if exists key `pretrain`.
pretrain (str, dict): Describes how to load pretrained model.
str, treat pretrain as model path;
dict: should contains key `path`, and other parameters token by function load_pretrained();
"""
if not isinstance(cfg, Config):
raise TypeError(f'Config must be type dict, got {type(cfg)}')
model = build_from_config(cfg, registry, logger=logger, *args, **kwargs)
return model
# reigister cls for diffusion.
INFERENCES = Registry('INFERENCE', build_func=build_inference)
|